The 236-Tool SEO Mastery Booklet
Every tool a modern SEO professional needs — built from scratch with Python. Technical SEO, content strategy, AI visibility, performance, analytics, and advanced techniques.
236
Tools
20
Chapters
56K+
Words
6
Parts
Part I: Technical SEO
3 chapters · 36 tools
Chapter 1
Technical SEO Foundation
12 professional-grade tools you can build and deploy. Complete code, strategy, and implementation guides.
#1
Robots.txt Validator
Validates robots.txt syntax, checks for accidental blocks on critical pages, and simulates crawler behavior for Googlebot, Bingbot, GPTBot and PerplexityBot.
Technical SEO FoundationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract robots.txt validator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class Robots.TxtValidator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Robots.txt Validator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = Robots.TxtValidator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — robots.txt validator issues recur constantly. Automate weekly.
#2
XML Sitemap Generator
Crawls your site recursively, discovers all indexable URLs, and outputs a standards-compliant XML sitemap with priority scores and lastmod dates.
Technical SEO FoundationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract xml sitemap generator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class XmlSitemapGenerator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... XML Sitemap Generator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = XmlSitemapGenerator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — xml sitemap generator issues recur constantly. Automate weekly.
#3
Canonical Tag Checker
Audits every page for correct self-referencing canonicals, detects chains (A→B→C), and flags broken or conflicting canonical targets.
Technical SEO FoundationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract canonical tag checker signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class CanonicalTagChecker:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Canonical Tag Checker specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = CanonicalTagChecker("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — canonical tag checker issues recur constantly. Automate weekly.
#4
Redirect Chain Detector
Maps all redirect paths across your site, identifies chains longer than 2 hops, and calculates the PageRank dilution at each step.
Technical SEO FoundationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract redirect chain detector signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class RedirectChainDetector:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Redirect Chain Detector specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = RedirectChainDetector("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — redirect chain detector issues recur constantly. Automate weekly.
#5
HTTP Status Code Monitor
Continuously monitors HTTP status codes site-wide, alerting you instantly when pages return 4xx/5xx errors before they impact rankings.
Technical SEO FoundationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract http status code monitor signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class HttpStatusCodeMonitor:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... HTTP Status Code Monitor specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = HttpStatusCodeMonitor("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — http status code monitor issues recur constantly. Automate weekly.
#6
Crawl Budget Optimizer
Analyzes server logs to determine which pages receive crawl budget, identifies waste on low-value URLs, and recommends crawl directives.
Technical SEO FoundationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract crawl budget optimizer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class CrawlBudgetOptimizer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Crawl Budget Optimizer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = CrawlBudgetOptimizer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — crawl budget optimizer issues recur constantly. Automate weekly.
#7
URL Parameter Cleaner
Identifies URL parameters that create duplicate content, generates proper parameter handling rules, and submits them via Search Console API.
Technical SEO FoundationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract url parameter cleaner signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class UrlParameterCleaner:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... URL Parameter Cleaner specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = UrlParameterCleaner("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — url parameter cleaner issues recur constantly. Automate weekly.
#8
Hreflang Validator
Validates all hreflang annotations for correct language/region codes, bidirectional linking, and x-default fallback configuration.
Technical SEO FoundationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract hreflang validator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class HreflangValidator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Hreflang Validator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = HreflangValidator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — hreflang validator issues recur constantly. Automate weekly.
#9
Log File Analyzer
Parses web server access logs to reveal actual Googlebot behavior — what it crawls, how often, and what it ignores entirely.
Technical SEO FoundationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract log file analyzer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class LogFileAnalyzer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Log File Analyzer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = LogFileAnalyzer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — log file analyzer issues recur constantly. Automate weekly.
#10
Server Response Timer
Measures Time-to-First-Byte across all pages from multiple global locations, identifies slow backends, and benchmarks against competitors.
Technical SEO FoundationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract server response timer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ServerResponseTimer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Server Response Timer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ServerResponseTimer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — server response timer issues recur constantly. Automate weekly.
#11
DNS Configuration Checker
Audits DNS configuration for proper CNAME/A records, TTL settings, DNSSEC status, and identifies misconfigurations affecting site speed.
Technical SEO FoundationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract dns configuration checker signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class DnsConfigurationChecker:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... DNS Configuration Checker specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = DnsConfigurationChecker("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — dns configuration checker issues recur constantly. Automate weekly.
#12
SSL Certificate Monitor
Monitors SSL certificate expiry dates, checks for mixed content issues, validates certificate chain, and alerts 30 days before expiration.
Technical SEO FoundationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract ssl certificate monitor signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class SslCertificateMonitor:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... SSL Certificate Monitor specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = SslCertificateMonitor("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — ssl certificate monitor issues recur constantly. Automate weekly.
Chapter 2
On-Page SEO Essentials
12 professional-grade tools you can build and deploy. Complete code, strategy, and implementation guides.
#13
Meta Title Optimizer
Analyzes title tags for pixel width (not just characters), keyword placement, CTR power words, and detects duplicates across the site.
On-Page SEO EssentialsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract meta title optimizer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class MetaTitleOptimizer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Meta Title Optimizer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = MetaTitleOptimizer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — meta title optimizer issues recur constantly. Automate weekly.
#14
Meta Description Writer
Scores meta descriptions for length, keyword inclusion, CTA presence, and generates optimized alternatives based on page intent type.
On-Page SEO EssentialsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract meta description writer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class MetaDescriptionWriter:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Meta Description Writer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = MetaDescriptionWriter("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — meta description writer issues recur constantly. Automate weekly.
#15
Heading Hierarchy Checker
Validates H1-H6 nesting hierarchy, detects skipped levels, flags multiple H1s, and compares your heading structure against top-ranking competitors.
On-Page SEO EssentialsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract heading hierarchy checker signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class HeadingHierarchyChecker:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Heading Hierarchy Checker specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = HeadingHierarchyChecker("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — heading hierarchy checker issues recur constantly. Automate weekly.
#16
Image Alt Text Generator
Generates contextually relevant alt text using page content analysis, flags missing alts, and checks for keyword-stuffing patterns in existing alts.
On-Page SEO EssentialsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract image alt text generator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ImageAltTextGenerator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Image Alt Text Generator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ImageAltTextGenerator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — image alt text generator issues recur constantly. Automate weekly.
#17
Internal Link Mapper
Discovers all internal links, maps the complete link graph, identifies hub pages, and visualizes link equity flow through your site architecture.
On-Page SEO EssentialsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract internal link mapper signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class InternalLinkMapper:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Internal Link Mapper specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = InternalLinkMapper("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — internal link mapper issues recur constantly. Automate weekly.
#18
Keyword Density Analyzer
Calculates keyword density for target terms and related entities, warns about over-optimization, and compares against top-10 SERP averages.
On-Page SEO EssentialsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract keyword density analyzer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class KeywordDensityAnalyzer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Keyword Density Analyzer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = KeywordDensityAnalyzer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — keyword density analyzer issues recur constantly. Automate weekly.
#19
Content Length Optimizer
Benchmarks your page word counts against top-ranking competitors for each keyword, identifying pages that are too thin or unnecessarily bloated.
On-Page SEO EssentialsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract content length optimizer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ContentLengthOptimizer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Content Length Optimizer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ContentLengthOptimizer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — content length optimizer issues recur constantly. Automate weekly.
#20
Readability Score Calculator
Calculates Flesch-Kincaid, Gunning Fog, and SMOG readability scores, then recommends sentence/paragraph restructuring for your target audience.
On-Page SEO EssentialsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract readability score calculator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ReadabilityScoreCalculator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Readability Score Calculator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ReadabilityScoreCalculator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — readability score calculator issues recur constantly. Automate weekly.
#21
Duplicate Content Detector
Uses content fingerprinting (simhash) to detect near-duplicate pages across your site, identifying cannibalization before it tanks rankings.
On-Page SEO EssentialsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract duplicate content detector signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class DuplicateContentDetector:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Duplicate Content Detector specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = DuplicateContentDetector("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — duplicate content detector issues recur constantly. Automate weekly.
#22
Thin Content Finder
Identifies pages below 300 words with low unique value, categorizes them by fix strategy (expand, merge, redirect, or noindex).
On-Page SEO EssentialsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract thin content finder signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ThinContentFinder:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Thin Content Finder specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ThinContentFinder("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — thin content finder issues recur constantly. Automate weekly.
#23
Word Count Benchmarker
Tracks word count distribution across your content library and benchmarks each content type against industry standards.
On-Page SEO EssentialsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract word count benchmarker signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class WordCountBenchmarker:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Word Count Benchmarker specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = WordCountBenchmarker("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — word count benchmarker issues recur constantly. Automate weekly.
#24
Content Freshness Tracker
Monitors publication and modification dates, flags stale content over 12 months old, and prioritizes refresh candidates by traffic potential.
On-Page SEO EssentialsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract content freshness tracker signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ContentFreshnessTracker:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Content Freshness Tracker specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ContentFreshnessTracker("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — content freshness tracker issues recur constantly. Automate weekly.
Chapter 3
Schema & Structured Data
12 professional-grade tools you can build and deploy. Complete code, strategy, and implementation guides.
#25
Article Schema Generator
Produces valid JSON-LD Article schema with full author credentials, publisher info, datePublished/Modified, images, and speakable markup.
Schema & Structured DataDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract article schema generator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ArticleSchemaGenerator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Article Schema Generator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ArticleSchemaGenerator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — article schema generator issues recur constantly. Automate weekly.
#26
FAQ Schema Generator
Builds FAQ schema from your existing Q&A content, validates against Google's requirements, and outputs ready-to-paste JSON-LD.
Schema & Structured DataDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract faq schema generator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class FaqSchemaGenerator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... FAQ Schema Generator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = FaqSchemaGenerator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — faq schema generator issues recur constantly. Automate weekly.
#27
Organization Schema Builder
Creates comprehensive Organization schema including founding date, employee count, social profiles, knowsAbout, and award properties.
Schema & Structured DataDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract organization schema builder signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class OrganizationSchemaBuilder:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Organization Schema Builder specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = OrganizationSchemaBuilder("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — organization schema builder issues recur constantly. Automate weekly.
#28
LocalBusiness Schema Creator
Generates LocalBusiness schema with geo-coordinates, opening hours, payment methods, service area, and aggregate rating properties.
Schema & Structured DataDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract localbusiness schema creator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class LocalbusinessSchemaCreator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... LocalBusiness Schema Creator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = LocalbusinessSchemaCreator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — localbusiness schema creator issues recur constantly. Automate weekly.
#29
Product Schema Maker
Builds Product schema with offers, pricing, availability, brand, SKU, reviews, and GTIN — ready for Google Merchant rich results.
Schema & Structured DataDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract product schema maker signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ProductSchemaMaker:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Product Schema Maker specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ProductSchemaMaker("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — product schema maker issues recur constantly. Automate weekly.
#30
Review Schema Injector
Extracts existing reviews from your pages and wraps them in valid Review/AggregateRating schema with proper author attribution.
Schema & Structured DataDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract review schema injector signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ReviewSchemaInjector:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Review Schema Injector specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ReviewSchemaInjector("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — review schema injector issues recur constantly. Automate weekly.
#31
BreadcrumbList Generator
Generates BreadcrumbList schema from your URL structure, with proper position numbering and item names matching your nav hierarchy.
Schema & Structured DataDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract breadcrumblist generator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class BreadcrumblistGenerator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... BreadcrumbList Generator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = BreadcrumblistGenerator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — breadcrumblist generator issues recur constantly. Automate weekly.
#32
HowTo Schema Builder
Creates HowTo schema with steps, tools, supplies, time estimates, and images — qualifying your content for how-to rich results.
Schema & Structured DataDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract howto schema builder signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class HowtoSchemaBuilder:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... HowTo Schema Builder specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = HowtoSchemaBuilder("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — howto schema builder issues recur constantly. Automate weekly.
#33
Event Schema Creator
Builds Event schema with dates, location (physical or online), performer, offers, and organizer properties for event rich results.
Schema & Structured DataDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract event schema creator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class EventSchemaCreator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Event Schema Creator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = EventSchemaCreator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — event schema creator issues recur constantly. Automate weekly.
#34
Video Schema Generator
Generates VideoObject schema with thumbnail, duration, uploadDate, embedUrl, and transcript — enabling video rich results in SERPs.
Schema & Structured DataDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract video schema generator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class VideoSchemaGenerator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Video Schema Generator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = VideoSchemaGenerator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — video schema generator issues recur constantly. Automate weekly.
#35
Recipe Schema Maker
Creates Recipe schema with ingredients, instructions, nutrition info, prep/cook time, and yield — for recipe carousel eligibility.
Schema & Structured DataDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract recipe schema maker signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class RecipeSchemaMaker:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Recipe Schema Maker specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = RecipeSchemaMaker("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — recipe schema maker issues recur constantly. Automate weekly.
#36
Speakable Schema Tool
Implements Speakable schema marking which content sections are optimized for voice assistant and AI audio responses.
Schema & Structured DataDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract speakable schema tool signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class SpeakableSchemaTool:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Speakable Schema Tool specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = SpeakableSchemaTool("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — speakable schema tool issues recur constantly. Automate weekly.
Part II: Content & Authority
3 chapters · 36 tools
Chapter 4
Content Optimization
12 professional-grade tools you can build and deploy. Complete code, strategy, and implementation guides.
#37
Topic Cluster Builder
Maps your content into topic clusters with pillar pages and supporting articles, identifying gaps in your topical coverage.
Content OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract topic cluster builder signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class TopicClusterBuilder:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Topic Cluster Builder specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = TopicClusterBuilder("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — topic cluster builder issues recur constantly. Automate weekly.
#38
Content Gap Analyzer
Compares your content library against top competitors to find keywords and topics they rank for that you haven't covered yet.
Content OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract content gap analyzer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ContentGapAnalyzer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Content Gap Analyzer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ContentGapAnalyzer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — content gap analyzer issues recur constantly. Automate weekly.
#39
Semantic Keyword Finder
Discovers semantically related keywords using NLP co-occurrence analysis, going beyond simple keyword variations to true entities.
Content OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract semantic keyword finder signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class SemanticKeywordFinder:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Semantic Keyword Finder specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = SemanticKeywordFinder("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — semantic keyword finder issues recur constantly. Automate weekly.
#40
TF-IDF Calculator
Calculates Term Frequency-Inverse Document Frequency scores to identify which terms make your content unique vs. generic.
Content OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract tf-idf calculator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class TfIdfCalculator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... TF-IDF Calculator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = TfIdfCalculator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — tf-idf calculator issues recur constantly. Automate weekly.
#41
Content Brief Generator
Generates complete content briefs with target keywords, heading structure, word count, competitor insights, and questions to answer.
Content OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract content brief generator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ContentBriefGenerator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Content Brief Generator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ContentBriefGenerator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — content brief generator issues recur constantly. Automate weekly.
#42
Competitor Content Auditor
Reverse-engineers competitor content strategies — their publishing frequency, topics, formats, lengths, and update patterns.
Content OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract competitor content auditor signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class CompetitorContentAuditor:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Competitor Content Auditor specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = CompetitorContentAuditor("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — competitor content auditor issues recur constantly. Automate weekly.
#43
Featured Snippet Optimizer
Analyzes current featured snippets for your target keywords and restructures your content to match the winning format (paragraph, list, table).
Content OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract featured snippet optimizer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class FeaturedSnippetOptimizer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Featured Snippet Optimizer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = FeaturedSnippetOptimizer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — featured snippet optimizer issues recur constantly. Automate weekly.
#44
People Also Ask Scraper
Scrapes Google's People Also Ask boxes for your keywords, building a complete question database for FAQ and content expansion.
Content OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract people also ask scraper signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class PeopleAlsoAskScraper:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... People Also Ask Scraper specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = PeopleAlsoAskScraper("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — people also ask scraper issues recur constantly. Automate weekly.
#45
Content Cannibalization Detector
Detects when multiple pages on your site compete for the same keyword, recommending merge, redirect, or differentiation strategies.
Content OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract content cannibalization detector signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ContentCannibalizationDetector:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Content Cannibalization Detector specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ContentCannibalizationDetector("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — content cannibalization detector issues recur constantly. Automate weekly.
#46
Evergreen Content Scorer
Scores content for evergreen potential based on topic seasonality, query stability, and historical search volume consistency.
Content OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract evergreen content scorer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class EvergreenContentScorer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Evergreen Content Scorer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = EvergreenContentScorer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — evergreen content scorer issues recur constantly. Automate weekly.
#47
Content ROI Calculator
Calculates the revenue contribution of each content piece by combining organic traffic, conversion rates, and customer lifetime value.
Content OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract content roi calculator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ContentRoiCalculator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Content ROI Calculator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ContentRoiCalculator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — content roi calculator issues recur constantly. Automate weekly.
#48
Content Decay Detector
Identifies content that has declined in traffic over time, prioritizes refresh candidates, and estimates traffic recovery potential.
Content OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract content decay detector signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ContentDecayDetector:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Content Decay Detector specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ContentDecayDetector("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — content decay detector issues recur constantly. Automate weekly.
Chapter 5
Internal Linking Strategy
12 professional-grade tools you can build and deploy. Complete code, strategy, and implementation guides.
#49
Link Equity Calculator
Calculates how link equity distributes through your site based on link position, anchor text, and page authority scores.
Internal Linking StrategyDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract link equity calculator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class LinkEquityCalculator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Link Equity Calculator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = LinkEquityCalculator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — link equity calculator issues recur constantly. Automate weekly.
#50
Orphan Page Finder
Discovers pages with zero internal links pointing to them — invisible to both crawlers and users, losing all ranking potential.
Internal Linking StrategyDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract orphan page finder signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class OrphanPageFinder:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Orphan Page Finder specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = OrphanPageFinder("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — orphan page finder issues recur constantly. Automate weekly.
#51
Anchor Text Optimizer
Audits anchor text distribution for internal links, identifies over-optimized exact-match patterns, and suggests natural variations.
Internal Linking StrategyDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract anchor text optimizer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class AnchorTextOptimizer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Anchor Text Optimizer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = AnchorTextOptimizer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — anchor text optimizer issues recur constantly. Automate weekly.
#52
Hub & Spoke Mapper
Visualizes your hub-and-spoke content architecture, identifies missing connections, and recommends new contextual links.
Internal Linking StrategyDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract hub & spoke mapper signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class HubAndSpokeMapper:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Hub & Spoke Mapper specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = HubAndSpokeMapper("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — hub & spoke mapper issues recur constantly. Automate weekly.
#53
Contextual Link Injector
Scans content for natural linking opportunities by matching entity mentions against your page inventory, then suggests contextual links.
Internal Linking StrategyDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract contextual link injector signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ContextualLinkInjector:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Contextual Link Injector specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ContextualLinkInjector("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — contextual link injector issues recur constantly. Automate weekly.
#54
Link Depth Analyzer
Maps the click-depth of every page from homepage, flagging important pages buried too deep (4+ clicks) for crawler discovery.
Internal Linking StrategyDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract link depth analyzer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class LinkDepthAnalyzer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Link Depth Analyzer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = LinkDepthAnalyzer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — link depth analyzer issues recur constantly. Automate weekly.
#55
PageRank Flow Simulator
Simulates how PageRank flows through your internal link graph, identifying pages that hoard equity and pages that are starved.
Internal Linking StrategyDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract pagerank flow simulator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class PagerankFlowSimulator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... PageRank Flow Simulator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = PagerankFlowSimulator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — pagerank flow simulator issues recur constantly. Automate weekly.
#56
Broken Internal Link Finder
Crawls all internal links and reports broken ones (404s), along with the linking page and anchor text for quick fixing.
Internal Linking StrategyDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract broken internal link finder signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class BrokenInternalLinkFinder:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Broken Internal Link Finder specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = BrokenInternalLinkFinder("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — broken internal link finder issues recur constantly. Automate weekly.
#57
Navigation Structure Auditor
Audits your main navigation, footer links, and breadcrumbs for SEO effectiveness — proper hierarchy, crawlability, and mobile UX.
Internal Linking StrategyDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract navigation structure auditor signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class NavigationStructureAuditor:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Navigation Structure Auditor specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = NavigationStructureAuditor("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — navigation structure auditor issues recur constantly. Automate weekly.
#58
Silo Architecture Builder
Designs optimal silo structures for your content taxonomy, grouping related pages and establishing proper cross-linking rules.
Internal Linking StrategyDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract silo architecture builder signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class SiloArchitectureBuilder:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Silo Architecture Builder specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = SiloArchitectureBuilder("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — silo architecture builder issues recur constantly. Automate weekly.
#59
Related Posts Engine
Analyzes content relationships to generate relevant 'related posts' recommendations that improve engagement and link equity distribution.
Internal Linking StrategyDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract related posts engine signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class RelatedPostsEngine:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Related Posts Engine specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = RelatedPostsEngine("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — related posts engine issues recur constantly. Automate weekly.
#60
Link Priority Scorer
Scores each internal link opportunity by combining target page authority, topical relevance, and current link equity deficit.
Internal Linking StrategyDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract link priority scorer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class LinkPriorityScorer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Link Priority Scorer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = LinkPriorityScorer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — link priority scorer issues recur constantly. Automate weekly.
Chapter 6
E-E-A-T Signals
12 professional-grade tools you can build and deploy. Complete code, strategy, and implementation guides.
#61
Author Authority Scorer
Evaluates author pages and bylines for E-E-A-T compliance: credentials displayed, publication history, social proof, and Schema markup.
E-E-A-T SignalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract author authority scorer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class AuthorAuthorityScorer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Author Authority Scorer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = AuthorAuthorityScorer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — author authority scorer issues recur constantly. Automate weekly.
#62
Expertise Signal Checker
Checks whether your content demonstrates first-hand expertise through specific examples, case studies, original data, and practical advice.
E-E-A-T SignalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract expertise signal checker signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ExpertiseSignalChecker:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Expertise Signal Checker specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ExpertiseSignalChecker("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — expertise signal checker issues recur constantly. Automate weekly.
#63
Trust Flow Analyzer
Analyzes trust signals across your site: SSL status, privacy policy, contact information, business registration, and third-party validations.
E-E-A-T SignalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract trust flow analyzer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class TrustFlowAnalyzer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Trust Flow Analyzer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = TrustFlowAnalyzer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — trust flow analyzer issues recur constantly. Automate weekly.
#64
Citation Builder
Discovers citation opportunities on authoritative sites, industry directories, and academic databases to build entity authority.
E-E-A-T SignalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract citation builder signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class CitationBuilder:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Citation Builder specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = CitationBuilder("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — citation builder issues recur constantly. Automate weekly.
#65
About Page Optimizer
Audits your About page for E-E-A-T effectiveness: team credentials, company history, awards, press mentions, and Schema markup completeness.
E-E-A-T SignalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract about page optimizer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class AboutPageOptimizer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... About Page Optimizer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = AboutPageOptimizer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — about page optimizer issues recur constantly. Automate weekly.
#66
Credentials Validator
Validates displayed credentials (certifications, degrees, awards) against authoritative sources and checks for proper Schema representation.
E-E-A-T SignalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract credentials validator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class CredentialsValidator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Credentials Validator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = CredentialsValidator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — credentials validator issues recur constantly. Automate weekly.
#67
Testimonial Schema Adder
Extracts customer testimonials and implements Review schema, displaying social proof that AI systems recognize as trust signals.
E-E-A-T SignalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract testimonial schema adder signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class TestimonialSchemaAdder:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Testimonial Schema Adder specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = TestimonialSchemaAdder("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — testimonial schema adder issues recur constantly. Automate weekly.
#68
Press Mention Tracker
Monitors media mentions of your brand across news sites, blogs, and industry publications — a key signal for entity authority.
E-E-A-T SignalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract press mention tracker signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class PressMentionTracker:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Press Mention Tracker specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = PressMentionTracker("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — press mention tracker issues recur constantly. Automate weekly.
#69
Thought Leadership Scorer
Scores thought leadership content for depth, originality, citation potential, and alignment with what AI systems recognize as expertise.
E-E-A-T SignalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract thought leadership scorer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ThoughtLeadershipScorer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Thought Leadership Scorer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ThoughtLeadershipScorer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — thought leadership scorer issues recur constantly. Automate weekly.
#70
Byline Consistency Checker
Checks that author bylines are consistent across all content, linked to proper author pages, and match external profile information.
E-E-A-T SignalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract byline consistency checker signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class BylineConsistencyChecker:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Byline Consistency Checker specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = BylineConsistencyChecker("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — byline consistency checker issues recur constantly. Automate weekly.
#71
Expert Author Markup Tool
Generates Person schema for expert authors with credentials, publications, affiliations, and sameAs links to external profiles.
E-E-A-T SignalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract expert author markup tool signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ExpertAuthorMarkupTool:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Expert Author Markup Tool specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ExpertAuthorMarkupTool("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — expert author markup tool issues recur constantly. Automate weekly.
#72
Trust Seal Implementer
Identifies opportunities to add trust seals (BBB, industry certifications, security badges) and implements them with proper Schema.
E-E-A-T SignalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract trust seal implementer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class TrustSealImplementer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Trust Seal Implementer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = TrustSealImplementer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — trust seal implementer issues recur constantly. Automate weekly.
Part III: AI & GEO
4 chapters · 48 tools
Chapter 7
AEO Fundamentals
12 professional-grade tools you can build and deploy. Complete code, strategy, and implementation guides.
#73
AI Answer Tracker
Monitors which AI systems (ChatGPT, Gemini, Perplexity) cite your content when answering questions in your domain.
AEO FundamentalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract ai answer tracker signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class AiAnswerTracker:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... AI Answer Tracker specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = AiAnswerTracker("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — ai answer tracker issues recur constantly. Automate weekly.
#74
Question-Answer Formatter
Restructures existing content into clear Q&A pairs optimized for direct answer extraction by AI systems and featured snippets.
AEO FundamentalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract question-answer formatter signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class QuestionAnswerFormatter:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Question-Answer Formatter specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = QuestionAnswerFormatter("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — question-answer formatter issues recur constantly. Automate weekly.
#75
Direct Answer Optimizer
Analyzes top-ranking direct answers and optimizes your content format (length, structure, specificity) to match winning patterns.
AEO FundamentalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract direct answer optimizer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class DirectAnswerOptimizer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Direct Answer Optimizer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = DirectAnswerOptimizer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — direct answer optimizer issues recur constantly. Automate weekly.
#76
Voice Search Adapter
Adapts content for voice search queries — conversational phrasing, concise answers, and speakable Schema implementation.
AEO FundamentalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract voice search adapter signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class VoiceSearchAdapter:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Voice Search Adapter specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = VoiceSearchAdapter("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — voice search adapter issues recur constantly. Automate weekly.
#77
Zero-Click Content Builder
Creates content specifically designed to satisfy user intent without requiring a click — positioning you as the definitive source.
AEO FundamentalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract zero-click content builder signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ZeroClickContentBuilder:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Zero-Click Content Builder specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ZeroClickContentBuilder("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — zero-click content builder issues recur constantly. Automate weekly.
#78
Knowledge Panel Optimizer
Optimizes your brand entity for Google Knowledge Panel display: consistent NAP, Wikipedia presence, Wikidata claims, and Schema.
AEO FundamentalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract knowledge panel optimizer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class KnowledgePanelOptimizer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Knowledge Panel Optimizer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = KnowledgePanelOptimizer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — knowledge panel optimizer issues recur constantly. Automate weekly.
#79
Entity Definition Writer
Writes clear, authoritative entity definitions that AI systems can extract and present as definitive answers about your brand/topic.
AEO FundamentalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract entity definition writer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class EntityDefinitionWriter:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Entity Definition Writer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = EntityDefinitionWriter("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — entity definition writer issues recur constantly. Automate weekly.
#80
Concise Answer Generator
Generates concise 40-60 word answers for your target queries that match the format AI systems prefer for direct citation.
AEO FundamentalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract concise answer generator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ConciseAnswerGenerator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Concise Answer Generator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ConciseAnswerGenerator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — concise answer generator issues recur constantly. Automate weekly.
#81
Featured Answer Tester
Tests your content against live AI systems to verify whether you're being selected as the answer source for target queries.
AEO FundamentalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract featured answer tester signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class FeaturedAnswerTester:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Featured Answer Tester specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = FeaturedAnswerTester("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — featured answer tester issues recur constantly. Automate weekly.
#82
Answer Box Qualifier
Scores content sections on their likelihood of qualifying for Google's answer box based on format, authority, and directness.
AEO FundamentalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract answer box qualifier signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class AnswerBoxQualifier:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Answer Box Qualifier specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = AnswerBoxQualifier("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — answer box qualifier issues recur constantly. Automate weekly.
#83
Conversational Query Mapper
Maps conversational query patterns (how people actually ask AI assistants) and optimizes content to match natural language intent.
AEO FundamentalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract conversational query mapper signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ConversationalQueryMapper:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Conversational Query Mapper specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ConversationalQueryMapper("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — conversational query mapper issues recur constantly. Automate weekly.
#84
AI Citation Checker
Verifies that AI systems properly cite and attribute your content when using it in their generated responses.
AEO FundamentalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract ai citation checker signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class AiCitationChecker:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... AI Citation Checker specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = AiCitationChecker("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — ai citation checker issues recur constantly. Automate weekly.
Chapter 8
GEO Optimization
12 professional-grade tools you can build and deploy. Complete code, strategy, and implementation guides.
#85
LLM Mention Monitor
Tracks how often your brand is mentioned in ChatGPT, Claude, Perplexity, and Gemini responses across your target query set.
GEO OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract llm mention monitor signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class LlmMentionMonitor:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... LLM Mention Monitor specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = LlmMentionMonitor("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — llm mention monitor issues recur constantly. Automate weekly.
#86
Citation Frequency Tracker
Measures how frequently your URLs appear as cited sources in AI-generated responses compared to competitors.
GEO OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract citation frequency tracker signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class CitationFrequencyTracker:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Citation Frequency Tracker specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = CitationFrequencyTracker("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — citation frequency tracker issues recur constantly. Automate weekly.
#87
AI Source Evaluator
Evaluates what makes AI systems select specific sources, analyzing patterns in content that gets cited vs. ignored.
GEO OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract ai source evaluator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class AiSourceEvaluator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... AI Source Evaluator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = AiSourceEvaluator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — ai source evaluator issues recur constantly. Automate weekly.
#88
Training Data Signal Builder
Creates content signals that increase likelihood of inclusion in future AI model training data updates.
GEO OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract training data signal builder signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class TrainingDataSignalBuilder:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Training Data Signal Builder specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = TrainingDataSignalBuilder("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — training data signal builder issues recur constantly. Automate weekly.
#89
Cross-Platform Visibility Checker
Monitors your visibility across all 5 major AI platforms simultaneously, tracking mention rate changes over time.
GEO OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract cross-platform visibility checker signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class CrossPlatformVisibilityChecker:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Cross-Platform Visibility Checker specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = CrossPlatformVisibilityChecker("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — cross-platform visibility checker issues recur constantly. Automate weekly.
#90
Generative Response Analyzer
Deconstructs AI-generated responses to understand which sources were synthesized and how your content could be included.
GEO OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract generative response analyzer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class GenerativeResponseAnalyzer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Generative Response Analyzer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = GenerativeResponseAnalyzer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — generative response analyzer issues recur constantly. Automate weekly.
#91
AI Crawl Permissions Manager
Configures robots.txt and meta tags to explicitly allow AI crawlers (GPTBot, PerplexityBot, ClaudeBot) while blocking unwanted bots.
GEO OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract ai crawl permissions manager signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class AiCrawlPermissionsManager:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... AI Crawl Permissions Manager specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = AiCrawlPermissionsManager("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — ai crawl permissions manager issues recur constantly. Automate weekly.
#92
Content Citability Scorer
Scores each piece of content for 'citability' — whether it contains specific, quotable data points that AI systems prefer to reference.
GEO OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract content citability scorer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ContentCitabilityScorer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Content Citability Scorer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ContentCitabilityScorer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — content citability scorer issues recur constantly. Automate weekly.
#93
AI Snippet Extractor
Extracts the specific text snippets that AI systems quote from your pages, identifying patterns in what gets selected.
GEO OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract ai snippet extractor signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class AiSnippetExtractor:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... AI Snippet Extractor specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = AiSnippetExtractor("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — ai snippet extractor issues recur constantly. Automate weekly.
#94
Source Authority Amplifier
Builds cross-web authority signals (PR, publications, structured data) that make AI systems trust your content more.
GEO OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract source authority amplifier signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class SourceAuthorityAmplifier:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Source Authority Amplifier specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = SourceAuthorityAmplifier("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — source authority amplifier issues recur constantly. Automate weekly.
#95
GEO Share of Voice Tool
Calculates your brand's share of voice in AI responses vs. competitors for your target keyword set.
GEO OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract geo share of voice tool signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class GeoShareOfVoiceTool:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... GEO Share of Voice Tool specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = GeoShareOfVoiceTool("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — geo share of voice tool issues recur constantly. Automate weekly.
#96
AI Response Position Tracker
Tracks where in the AI response your brand appears — first recommendation, middle mention, or end-of-list footnote.
GEO OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract ai response position tracker signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class AiResponsePositionTracker:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... AI Response Position Tracker specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = AiResponsePositionTracker("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — ai response position tracker issues recur constantly. Automate weekly.
Chapter 9
AI Citation Building
12 professional-grade tools you can build and deploy. Complete code, strategy, and implementation guides.
#97
Wikipedia Readiness Checker
Evaluates whether your brand/entity meets Wikipedia's notability criteria and identifies gaps to address before submission.
AI Citation BuildingDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract wikipedia readiness checker signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class WikipediaReadinessChecker:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Wikipedia Readiness Checker specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = WikipediaReadinessChecker("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — wikipedia readiness checker issues recur constantly. Automate weekly.
#98
Wikidata Entity Creator
Creates structured Wikidata entries for your entity with proper claims, references, and relationship properties.
AI Citation BuildingDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract wikidata entity creator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class WikidataEntityCreator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Wikidata Entity Creator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = WikidataEntityCreator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — wikidata entity creator issues recur constantly. Automate weekly.
#99
Knowledge Graph Builder
Builds entity relationships in Google's Knowledge Graph through structured data, Wikipedia, and cross-web consistency.
AI Citation BuildingDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract knowledge graph builder signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class KnowledgeGraphBuilder:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Knowledge Graph Builder specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = KnowledgeGraphBuilder("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — knowledge graph builder issues recur constantly. Automate weekly.
#100
Structured Claim Formatter
Formats your claims and statistics as structured, verifiable statements that AI systems can confidently cite with attribution.
AI Citation BuildingDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract structured claim formatter signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class StructuredClaimFormatter:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Structured Claim Formatter specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = StructuredClaimFormatter("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — structured claim formatter issues recur constantly. Automate weekly.
#101
Data Point Publisher
Publishes unique data points (research, surveys, benchmarks) in formats optimized for AI retrieval and citation.
AI Citation BuildingDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract data point publisher signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class DataPointPublisher:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Data Point Publisher specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = DataPointPublisher("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — data point publisher issues recur constantly. Automate weekly.
#102
Quotable Stat Generator
Generates specific, quotable statistics from your data that AI systems will preferentially cite due to their uniqueness.
AI Citation BuildingDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract quotable stat generator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class QuotableStatGenerator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Quotable Stat Generator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = QuotableStatGenerator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — quotable stat generator issues recur constantly. Automate weekly.
#103
Press Release AI Optimizer
Optimizes press releases with structured data and citable claims designed for pickup by AI training pipelines.
AI Citation BuildingDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract press release ai optimizer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class PressReleaseAiOptimizer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Press Release AI Optimizer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = PressReleaseAiOptimizer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — press release ai optimizer issues recur constantly. Automate weekly.
#104
Research Report Builder
Produces original research reports with methodology, sample sizes, and specific findings that become citation magnets.
AI Citation BuildingDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract research report builder signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ResearchReportBuilder:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Research Report Builder specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ResearchReportBuilder("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — research report builder issues recur constantly. Automate weekly.
#105
Expert Quote Distributor
Distributes expert quotes across authoritative platforms where AI systems will discover and associate them with your entity.
AI Citation BuildingDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract expert quote distributor signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ExpertQuoteDistributor:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Expert Quote Distributor specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ExpertQuoteDistributor("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — expert quote distributor issues recur constantly. Automate weekly.
#106
Entity Consistency Auditor
Audits entity information consistency across 20+ platforms, fixing discrepancies that reduce AI confidence in your claims.
AI Citation BuildingDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract entity consistency auditor signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class EntityConsistencyAuditor:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Entity Consistency Auditor specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = EntityConsistencyAuditor("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — entity consistency auditor issues recur constantly. Automate weekly.
#107
Cross-Reference Validator
Validates that claims on your site match what third-party sources say, building the cross-reference trust that AI systems require.
AI Citation BuildingDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract cross-reference validator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class CrossReferenceValidator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Cross-Reference Validator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = CrossReferenceValidator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — cross-reference validator issues recur constantly. Automate weekly.
#108
Citation Chain Builder
Maps how citations flow between your content and AI systems, optimizing the chain from publication to AI recommendation.
AI Citation BuildingDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract citation chain builder signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class CitationChainBuilder:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Citation Chain Builder specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = CitationChainBuilder("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — citation chain builder issues recur constantly. Automate weekly.
Chapter 10
LLM Prompt Alignment
12 professional-grade tools you can build and deploy. Complete code, strategy, and implementation guides.
#109
Prompt Response Simulator
Tests how different prompts cause AI systems to respond about your brand/category, revealing positioning opportunities.
LLM Prompt AlignmentDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract prompt response simulator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class PromptResponseSimulator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Prompt Response Simulator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = PromptResponseSimulator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — prompt response simulator issues recur constantly. Automate weekly.
#110
Brand Mention Tester
Queries multiple AI systems about your brand and tracks mention rates, accuracy, and sentiment over time.
LLM Prompt AlignmentDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract brand mention tester signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class BrandMentionTester:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Brand Mention Tester specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = BrandMentionTester("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — brand mention tester issues recur constantly. Automate weekly.
#111
Category Association Builder
Builds content that creates strong associations between your brand and target category keywords in AI training data.
LLM Prompt AlignmentDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract category association builder signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class CategoryAssociationBuilder:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Category Association Builder specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = CategoryAssociationBuilder("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — category association builder issues recur constantly. Automate weekly.
#112
Competitive Prompt Analyzer
Analyzes how competitors are positioned in AI responses and identifies differentiation opportunities in LLM output.
LLM Prompt AlignmentDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract competitive prompt analyzer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class CompetitivePromptAnalyzer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Competitive Prompt Analyzer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = CompetitivePromptAnalyzer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — competitive prompt analyzer issues recur constantly. Automate weekly.
#113
Response Sentiment Monitor
Monitors the sentiment and accuracy of what AI systems say about your brand, flagging inaccuracies for correction.
LLM Prompt AlignmentDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract response sentiment monitor signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ResponseSentimentMonitor:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Response Sentiment Monitor specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ResponseSentimentMonitor("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — response sentiment monitor issues recur constantly. Automate weekly.
#114
AI Persona Aligner
Aligns your digital presence with how AI personas (helpful assistant, expert advisor) naturally want to recommend solutions.
LLM Prompt AlignmentDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract ai persona aligner signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class AiPersonaAligner:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... AI Persona Aligner specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = AiPersonaAligner("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — ai persona aligner issues recur constantly. Automate weekly.
#115
Multi-LLM Query Tester
Tests identical queries across ChatGPT, Claude, Gemini, Perplexity, and Copilot to map platform-specific differences.
LLM Prompt AlignmentDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract multi-llm query tester signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class MultiLlmQueryTester:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Multi-LLM Query Tester specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = MultiLlmQueryTester("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — multi-llm query tester issues recur constantly. Automate weekly.
#116
Prompt Pattern Mapper
Identifies patterns in how users phrase prompts in your category, optimizing content to match those natural patterns.
LLM Prompt AlignmentDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract prompt pattern mapper signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class PromptPatternMapper:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Prompt Pattern Mapper specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = PromptPatternMapper("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — prompt pattern mapper issues recur constantly. Automate weekly.
#117
AI Recommendation Tracker
Tracks whether AI systems recommend your brand/product when users ask for suggestions in your category.
LLM Prompt AlignmentDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract ai recommendation tracker signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class AiRecommendationTracker:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... AI Recommendation Tracker specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = AiRecommendationTracker("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — ai recommendation tracker issues recur constantly. Automate weekly.
#118
Brand Narrative Validator
Validates that the narrative AI tells about your brand matches your actual positioning and corrects misalignments.
LLM Prompt AlignmentDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract brand narrative validator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class BrandNarrativeValidator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Brand Narrative Validator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = BrandNarrativeValidator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — brand narrative validator issues recur constantly. Automate weekly.
#119
Context Window Optimizer
Optimizes content length and structure to fit within AI retrieval context windows for maximum citation probability.
LLM Prompt AlignmentDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract context window optimizer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ContextWindowOptimizer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Context Window Optimizer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ContextWindowOptimizer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — context window optimizer issues recur constantly. Automate weekly.
#120
Training Signal Amplifier
Creates signals across the web that reinforce your brand associations in future AI model training updates.
LLM Prompt AlignmentDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract training signal amplifier signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class TrainingSignalAmplifier:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Training Signal Amplifier specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = TrainingSignalAmplifier("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — training signal amplifier issues recur constantly. Automate weekly.
Part IV: Performance
3 chapters · 36 tools
Chapter 11
Core Web Vitals
12 professional-grade tools you can build and deploy. Complete code, strategy, and implementation guides.
#121
LCP Optimizer
Identifies and fixes Largest Contentful Paint issues — optimizing hero images, server response, and render-blocking resources.
Core Web VitalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract lcp optimizer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class LcpOptimizer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... LCP Optimizer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = LcpOptimizer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — lcp optimizer issues recur constantly. Automate weekly.
#122
FID Reducer
Measures and reduces First Input Delay by identifying long JavaScript tasks that block the main thread during interaction.
Core Web VitalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract fid reducer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class FidReducer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... FID Reducer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = FidReducer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — fid reducer issues recur constantly. Automate weekly.
#123
CLS Fixer
Detects and eliminates Cumulative Layout Shift by auditing dynamic content, images without dimensions, and late-loading fonts.
Core Web VitalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract cls fixer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ClsFixer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... CLS Fixer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ClsFixer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — cls fixer issues recur constantly. Automate weekly.
#124
INP Analyzer
Analyzes Interaction to Next Paint responsiveness, identifying event handlers that take too long to process user interactions.
Core Web VitalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract inp analyzer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class InpAnalyzer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... INP Analyzer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = InpAnalyzer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — inp analyzer issues recur constantly. Automate weekly.
#125
TTFB Monitor
Monitors Time to First Byte across pages and geographic locations, pinpointing backend bottlenecks and hosting issues.
Core Web VitalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract ttfb monitor signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class TtfbMonitor:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... TTFB Monitor specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = TtfbMonitor("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — ttfb monitor issues recur constantly. Automate weekly.
#126
Performance Budget Calculator
Creates performance budgets for page weight, request count, and JavaScript size — alerting when new deploys exceed limits.
Core Web VitalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract performance budget calculator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class PerformanceBudgetCalculator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Performance Budget Calculator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = PerformanceBudgetCalculator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — performance budget calculator issues recur constantly. Automate weekly.
#127
Layout Shift Detector
Records and reports every layout shift event with its cause (late images, injected ads, font swaps) and impact score.
Core Web VitalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract layout shift detector signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class LayoutShiftDetector:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Layout Shift Detector specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = LayoutShiftDetector("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — layout shift detector issues recur constantly. Automate weekly.
#128
Long Task Identifier
Identifies JavaScript Long Tasks (>50ms) that block the main thread, with stack traces showing exactly which code is responsible.
Core Web VitalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract long task identifier signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class LongTaskIdentifier:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Long Task Identifier specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = LongTaskIdentifier("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — long task identifier issues recur constantly. Automate weekly.
#129
Resource Hint Generator
Generates optimal resource hints (preload, prefetch, preconnect, dns-prefetch) based on actual page resource loading patterns.
Core Web VitalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract resource hint generator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ResourceHintGenerator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Resource Hint Generator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ResourceHintGenerator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — resource hint generator issues recur constantly. Automate weekly.
#130
Render-Blocking Finder
Finds CSS and JavaScript that blocks initial render, recommending critical path extraction and async/defer loading strategies.
Core Web VitalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract render-blocking finder signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class RenderBlockingFinder:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Render-Blocking Finder specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = RenderBlockingFinder("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — render-blocking finder issues recur constantly. Automate weekly.
#131
Paint Timing Logger
Logs First Paint and First Contentful Paint timings via Performance Observer API, tracking trends across deployments.
Core Web VitalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract paint timing logger signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class PaintTimingLogger:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Paint Timing Logger specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = PaintTimingLogger("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — paint timing logger issues recur constantly. Automate weekly.
#132
Vitals Score Predictor
Predicts Core Web Vitals scores for new pages based on their resource patterns, template type, and historical performance data.
Core Web VitalsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract vitals score predictor signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class VitalsScorePredictor:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Vitals Score Predictor specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = VitalsScorePredictor("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — vitals score predictor issues recur constantly. Automate weekly.
Chapter 12
Page Speed Tools
12 professional-grade tools you can build and deploy. Complete code, strategy, and implementation guides.
#133
Image Compression Pipeline
Automates image optimization pipeline: converts to WebP/AVIF, resizes to exact display dimensions, and applies optimal compression.
Page Speed ToolsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract image compression pipeline signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ImageCompressionPipeline:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Image Compression Pipeline specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ImageCompressionPipeline("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — image compression pipeline issues recur constantly. Automate weekly.
#134
Lazy Load Implementer
Implements Intersection Observer-based lazy loading for images and iframes, with proper placeholder sizing to prevent CLS.
Page Speed ToolsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract lazy load implementer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class LazyLoadImplementer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Lazy Load Implementer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = LazyLoadImplementer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — lazy load implementer issues recur constantly. Automate weekly.
#135
Critical CSS Extractor
Extracts above-the-fold critical CSS automatically, inlining it in the HTML head and deferring the rest asynchronously.
Page Speed ToolsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract critical css extractor signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class CriticalCssExtractor:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Critical CSS Extractor specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = CriticalCssExtractor("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — critical css extractor issues recur constantly. Automate weekly.
#136
JavaScript Bundle Analyzer
Analyzes JavaScript bundles for unused code, duplicate dependencies, and opportunities for code-splitting and tree-shaking.
Page Speed ToolsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract javascript bundle analyzer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class JavascriptBundleAnalyzer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... JavaScript Bundle Analyzer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = JavascriptBundleAnalyzer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — javascript bundle analyzer issues recur constantly. Automate weekly.
#137
Font Loading Optimizer
Optimizes web font loading with font-display strategies, subset generation, and preload directives for critical fonts.
Page Speed ToolsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract font loading optimizer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class FontLoadingOptimizer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Font Loading Optimizer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = FontLoadingOptimizer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — font loading optimizer issues recur constantly. Automate weekly.
#138
CDN Configuration Checker
Audits CDN configuration for proper cache headers, compression settings, geographic coverage, and failover behavior.
Page Speed ToolsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract cdn configuration checker signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class CdnConfigurationChecker:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... CDN Configuration Checker specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = CdnConfigurationChecker("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — cdn configuration checker issues recur constantly. Automate weekly.
#139
Cache Header Generator
Generates optimal Cache-Control and ETag headers for each resource type, maximizing cache hits while ensuring freshness.
Page Speed ToolsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract cache header generator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class CacheHeaderGenerator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Cache Header Generator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = CacheHeaderGenerator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — cache header generator issues recur constantly. Automate weekly.
#140
Preload Priority Setter
Determines which resources benefit from preload hints based on critical rendering path analysis and actual load waterfalls.
Page Speed ToolsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract preload priority setter signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class PreloadPrioritySetter:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Preload Priority Setter specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = PreloadPrioritySetter("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — preload priority setter issues recur constantly. Automate weekly.
#141
Service Worker Builder
Builds a service worker for offline caching, background sync, and smart cache strategies (stale-while-revalidate, cache-first).
Page Speed ToolsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract service worker builder signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ServiceWorkerBuilder:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Service Worker Builder specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ServiceWorkerBuilder("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — service worker builder issues recur constantly. Automate weekly.
#142
HTTP/2 Push Configurator
Configures HTTP/2 Server Push for critical resources, with proper cache-digest awareness to avoid pushing already-cached assets.
Page Speed ToolsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract http/2 push configurator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class Http2PushConfigurator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... HTTP/2 Push Configurator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = Http2PushConfigurator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — http/2 push configurator issues recur constantly. Automate weekly.
#143
Asset Minification Pipeline
Automates minification of HTML, CSS, and JavaScript with source maps for debugging — integrated into your build pipeline.
Page Speed ToolsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract asset minification pipeline signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class AssetMinificationPipeline:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Asset Minification Pipeline specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = AssetMinificationPipeline("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — asset minification pipeline issues recur constantly. Automate weekly.
#144
Third-Party Script Auditor
Audits third-party scripts for performance impact: render-blocking behavior, payload size, and execution time per vendor.
Page Speed ToolsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract third-party script auditor signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ThirdPartyScriptAuditor:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Third-Party Script Auditor specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ThirdPartyScriptAuditor("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — third-party script auditor issues recur constantly. Automate weekly.
Chapter 13
Mobile Optimization
12 professional-grade tools you can build and deploy. Complete code, strategy, and implementation guides.
#145
Mobile-First Validator
Validates that your site passes Google's mobile-first indexing requirements: viewport, text size, tap targets, content parity.
Mobile OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract mobile-first validator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class MobileFirstValidator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Mobile-First Validator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = MobileFirstValidator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — mobile-first validator issues recur constantly. Automate weekly.
#146
Viewport Configuration Checker
Checks viewport meta tag configuration for proper width, initial-scale, and user-scalable settings across all page templates.
Mobile OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract viewport configuration checker signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ViewportConfigurationChecker:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Viewport Configuration Checker specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ViewportConfigurationChecker("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — viewport configuration checker issues recur constantly. Automate weekly.
#147
Touch Target Sizer
Measures all interactive elements against the 48x48px minimum touch target size with 8px spacing requirements.
Mobile OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract touch target sizer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class TouchTargetSizer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Touch Target Sizer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = TouchTargetSizer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — touch target sizer issues recur constantly. Automate weekly.
#148
Mobile Speed Tester
Runs Lighthouse mobile audits at scale, scoring every page template and identifying the lowest-performing mobile experiences.
Mobile OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract mobile speed tester signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class MobileSpeedTester:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Mobile Speed Tester specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = MobileSpeedTester("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — mobile speed tester issues recur constantly. Automate weekly.
#149
Responsive Image Generator
Generates responsive images with srcset and sizes attributes, creating multiple resolutions and serving optimal versions per device.
Mobile OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract responsive image generator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ResponsiveImageGenerator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Responsive Image Generator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ResponsiveImageGenerator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — responsive image generator issues recur constantly. Automate weekly.
#150
AMP Validator
Validates AMP pages against the latest AMP specification, checking for disallowed tags, scripts, and styling issues.
Mobile OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract amp validator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class AmpValidator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... AMP Validator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = AmpValidator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — amp validator issues recur constantly. Automate weekly.
#151
Mobile UX Scorer
Scores mobile user experience across dimensions: readability, navigation ease, form usability, and interaction responsiveness.
Mobile OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract mobile ux scorer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class MobileUxScorer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Mobile UX Scorer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = MobileUxScorer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — mobile ux scorer issues recur constantly. Automate weekly.
#152
Tap Delay Eliminator
Eliminates the 300ms tap delay on mobile by implementing proper touch-action CSS and passive event listeners.
Mobile OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract tap delay eliminator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class TapDelayEliminator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Tap Delay Eliminator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = TapDelayEliminator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — tap delay eliminator issues recur constantly. Automate weekly.
#153
Mobile Redirect Checker
Detects mobile-specific redirects (m-dot, separate URLs) and validates proper alternate/canonical annotations between versions.
Mobile OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract mobile redirect checker signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class MobileRedirectChecker:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Mobile Redirect Checker specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = MobileRedirectChecker("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — mobile redirect checker issues recur constantly. Automate weekly.
#154
Thumb Zone Mapper
Maps interactive elements to thumb-reachable zones on common device sizes, recommending layout adjustments for one-handed use.
Mobile OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract thumb zone mapper signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ThumbZoneMapper:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Thumb Zone Mapper specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ThumbZoneMapper("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — thumb zone mapper issues recur constantly. Automate weekly.
#155
Mobile Font Scaler
Audits font sizes across breakpoints, ensuring body text meets minimum 16px and line-height 1.5 requirements on all devices.
Mobile OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract mobile font scaler signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class MobileFontScaler:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Mobile Font Scaler specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = MobileFontScaler("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — mobile font scaler issues recur constantly. Automate weekly.
#156
Progressive Web App Builder
Builds a Progressive Web App with manifest.json, service worker, and installability criteria — enabling app-like mobile experience.
Mobile OptimizationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract progressive web app builder signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ProgressiveWebAppBuilder:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Progressive Web App Builder specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ProgressiveWebAppBuilder("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — progressive web app builder issues recur constantly. Automate weekly.
Part V: Analytics & Tracking
3 chapters · 36 tools
Chapter 14
Search Console Mastery
12 professional-grade tools you can build and deploy. Complete code, strategy, and implementation guides.
#157
GSC Data Exporter
Exports Search Console data via API for custom analysis: queries, pages, devices, countries, and date ranges into your data warehouse.
Search Console MasteryDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract gsc data exporter signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class GscDataExporter:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... GSC Data Exporter specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = GscDataExporter("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — gsc data exporter issues recur constantly. Automate weekly.
#158
Click Curve Analyzer
Analyzes CTR by position to find underperforming pages where ranking is strong but clicks are low — revealing title/description issues.
Search Console MasteryDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract click curve analyzer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ClickCurveAnalyzer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Click Curve Analyzer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ClickCurveAnalyzer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — click curve analyzer issues recur constantly. Automate weekly.
#159
Impression Gap Finder
Finds keywords where you have impressions but low rankings, identifying the easiest opportunities to move from page 2 to page 1.
Search Console MasteryDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract impression gap finder signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ImpressionGapFinder:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Impression Gap Finder specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ImpressionGapFinder("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — impression gap finder issues recur constantly. Automate weekly.
#160
CTR Optimizer
Optimizes click-through rates by testing title tag and meta description variations for pages with high impressions but below-average CTR.
Search Console MasteryDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract ctr optimizer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class CtrOptimizer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... CTR Optimizer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = CtrOptimizer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — ctr optimizer issues recur constantly. Automate weekly.
#161
Index Coverage Monitor
Monitors index coverage status across all pages, alerting on new exclusions, crawl anomalies, and indexing errors.
Search Console MasteryDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract index coverage monitor signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class IndexCoverageMonitor:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Index Coverage Monitor specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = IndexCoverageMonitor("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — index coverage monitor issues recur constantly. Automate weekly.
#162
Manual Action Checker
Checks for manual actions and security issues, with historical tracking to correlate ranking drops with potential penalties.
Search Console MasteryDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract manual action checker signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ManualActionChecker:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Manual Action Checker specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ManualActionChecker("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — manual action checker issues recur constantly. Automate weekly.
#163
Rich Result Tracker
Tracks rich result eligibility and appearance rates for your structured data implementations across all supported types.
Search Console MasteryDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract rich result tracker signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class RichResultTracker:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Rich Result Tracker specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = RichResultTracker("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — rich result tracker issues recur constantly. Automate weekly.
#164
Core Update Impact Analyzer
Correlates ranking changes with known Google algorithm updates, identifying which updates impacted your site and which pages.
Search Console MasteryDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract core update impact analyzer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class CoreUpdateImpactAnalyzer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Core Update Impact Analyzer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = CoreUpdateImpactAnalyzer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — core update impact analyzer issues recur constantly. Automate weekly.
#165
Query Clustering Tool
Groups related queries into semantic clusters, revealing your true topic authority and identifying cluster gaps.
Search Console MasteryDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract query clustering tool signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class QueryClusteringTool:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Query Clustering Tool specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = QueryClusteringTool("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — query clustering tool issues recur constantly. Automate weekly.
#166
Page-Level Performance Mapper
Maps Search Console performance data to individual page URLs, showing exactly which pages drive traffic for which queries.
Search Console MasteryDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract page-level performance mapper signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class PageLevelPerformanceMapper:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Page-Level Performance Mapper specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = PageLevelPerformanceMapper("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — page-level performance mapper issues recur constantly. Automate weekly.
#167
Crawl Stats Dashboard
Visualizes crawl stats (requests/day, response times, download sizes) to identify crawl budget issues and server bottlenecks.
Search Console MasteryDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract crawl stats dashboard signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class CrawlStatsDashboard:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Crawl Stats Dashboard specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = CrawlStatsDashboard("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — crawl stats dashboard issues recur constantly. Automate weekly.
#168
Sitemap Submission Automator
Automates sitemap submission after content updates, triggering re-crawl requests for new and updated pages.
Search Console MasteryDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract sitemap submission automator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class SitemapSubmissionAutomator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Sitemap Submission Automator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = SitemapSubmissionAutomator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — sitemap submission automator issues recur constantly. Automate weekly.
Chapter 15
Rank Tracking Systems
12 professional-grade tools you can build and deploy. Complete code, strategy, and implementation guides.
#169
SERP Position Monitor
Monitors keyword positions daily across Google, Bing, and AI platforms — with historical trending and competitor comparison.
Rank Tracking SystemsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract serp position monitor signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class SerpPositionMonitor:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... SERP Position Monitor specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = SerpPositionMonitor("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — serp position monitor issues recur constantly. Automate weekly.
#170
Keyword Volatility Tracker
Measures day-to-day ranking volatility for your keywords, identifying unstable positions that need content strengthening.
Rank Tracking SystemsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract keyword volatility tracker signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class KeywordVolatilityTracker:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Keyword Volatility Tracker specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = KeywordVolatilityTracker("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — keyword volatility tracker issues recur constantly. Automate weekly.
#171
Local Rank Checker
Tracks local pack and map rankings for geo-specific keywords across multiple locations and zip codes simultaneously.
Rank Tracking SystemsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract local rank checker signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class LocalRankChecker:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Local Rank Checker specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = LocalRankChecker("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — local rank checker issues recur constantly. Automate weekly.
#172
Featured Snippet Monitor
Monitors featured snippet ownership — alerts when you gain or lose snippets, and identifies new snippet opportunities.
Rank Tracking SystemsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract featured snippet monitor signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class FeaturedSnippetMonitor:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Featured Snippet Monitor specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = FeaturedSnippetMonitor("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — featured snippet monitor issues recur constantly. Automate weekly.
#173
SERP Feature Detector
Detects SERP features (PAA, video carousels, knowledge panels, AI overviews) appearing for your tracked keywords.
Rank Tracking SystemsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract serp feature detector signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class SerpFeatureDetector:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... SERP Feature Detector specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = SerpFeatureDetector("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — serp feature detector issues recur constantly. Automate weekly.
#174
Competitor Rank Comparator
Compares your rankings against top 5 competitors side-by-side, visualizing position gaps and crossover trends.
Rank Tracking SystemsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract competitor rank comparator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class CompetitorRankComparator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Competitor Rank Comparator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = CompetitorRankComparator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — competitor rank comparator issues recur constantly. Automate weekly.
#175
Rank Recovery Tracker
Tracks ranking recovery after drops, measuring how quickly positions return after content fixes or algorithm updates.
Rank Tracking SystemsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract rank recovery tracker signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class RankRecoveryTracker:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Rank Recovery Tracker specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = RankRecoveryTracker("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — rank recovery tracker issues recur constantly. Automate weekly.
#176
Position Change Alerter
Sends instant alerts via Slack/email when positions change by more than 3 spots for critical keywords.
Rank Tracking SystemsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract position change alerter signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class PositionChangeAlerter:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Position Change Alerter specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = PositionChangeAlerter("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — position change alerter issues recur constantly. Automate weekly.
#177
Keyword Cannibalization Mapper
Maps keywords where multiple pages from your site rank, identifying internal competition that dilutes authority.
Rank Tracking SystemsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract keyword cannibalization mapper signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class KeywordCannibalizationMapper:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Keyword Cannibalization Mapper specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = KeywordCannibalizationMapper("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — keyword cannibalization mapper issues recur constantly. Automate weekly.
#178
SERP History Logger
Maintains complete SERP history screenshots and composition changes over time for forensic ranking analysis.
Rank Tracking SystemsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract serp history logger signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class SerpHistoryLogger:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... SERP History Logger specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = SerpHistoryLogger("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — serp history logger issues recur constantly. Automate weekly.
#179
Ranking Forecast Model
Uses historical ranking data and trend analysis to predict future position changes and traffic impact.
Rank Tracking SystemsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract ranking forecast model signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class RankingForecastModel:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Ranking Forecast Model specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = RankingForecastModel("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — ranking forecast model issues recur constantly. Automate weekly.
#180
Mobile vs Desktop Rank Split
Compares mobile vs desktop rankings for each keyword, identifying split-ranking issues that affect mobile-first indexing.
Rank Tracking SystemsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract mobile vs desktop rank split signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class MobileVsDesktopRankSplit:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Mobile vs Desktop Rank Split specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = MobileVsDesktopRankSplit("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — mobile vs desktop rank split issues recur constantly. Automate weekly.
Chapter 16
Conversion Attribution
12 professional-grade tools you can build and deploy. Complete code, strategy, and implementation guides.
#181
Organic Landing Page Analyzer
Identifies which organic landing pages convert best and worst, with CRO recommendations for underperformers.
Conversion AttributionDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract organic landing page analyzer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class OrganicLandingPageAnalyzer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Organic Landing Page Analyzer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = OrganicLandingPageAnalyzer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — organic landing page analyzer issues recur constantly. Automate weekly.
#182
Goal Flow Mapper
Maps the complete user journey from organic entry to conversion, identifying exit points and friction in the funnel.
Conversion AttributionDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract goal flow mapper signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class GoalFlowMapper:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Goal Flow Mapper specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = GoalFlowMapper("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — goal flow mapper issues recur constantly. Automate weekly.
#183
Multi-Touch Attribution Builder
Builds multi-touch attribution models showing how organic search assists conversions across other channels (paid, social, direct).
Conversion AttributionDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract multi-touch attribution builder signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class MultiTouchAttributionBuilder:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Multi-Touch Attribution Builder specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = MultiTouchAttributionBuilder("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — multi-touch attribution builder issues recur constantly. Automate weekly.
#184
SEO Revenue Calculator
Calculates the dollar value of organic search traffic using conversion rates, average order values, and customer lifetime value.
Conversion AttributionDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract seo revenue calculator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class SeoRevenueCalculator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... SEO Revenue Calculator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = SeoRevenueCalculator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — seo revenue calculator issues recur constantly. Automate weekly.
#185
Assisted Conversion Tracker
Tracks conversions where organic search was an assist (not last-click), revealing SEO's true contribution to revenue.
Conversion AttributionDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract assisted conversion tracker signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class AssistedConversionTracker:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Assisted Conversion Tracker specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = AssistedConversionTracker("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — assisted conversion tracker issues recur constantly. Automate weekly.
#186
Funnel Drop-off Finder
Identifies where users drop off in conversion funnels after organic entry, pinpointing UX issues that waste SEO traffic.
Conversion AttributionDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract funnel drop-off finder signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class FunnelDropOffFinder:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Funnel Drop-off Finder specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = FunnelDropOffFinder("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — funnel drop-off finder issues recur constantly. Automate weekly.
#187
Event Tracking Implementer
Implements custom event tracking for micro-conversions (scroll depth, video plays, downloads) from organic visitors.
Conversion AttributionDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract event tracking implementer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class EventTrackingImplementer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Event Tracking Implementer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = EventTrackingImplementer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — event tracking implementer issues recur constantly. Automate weekly.
#188
UTM Builder & Validator
Generates properly formatted UTM parameters for tracking campaign sources, and validates existing URLs for parameter errors.
Conversion AttributionDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract utm builder & validator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class UtmBuilderAndValidator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... UTM Builder & Validator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = UtmBuilderAndValidator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — utm builder & validator issues recur constantly. Automate weekly.
#189
Session Recording Tagger
Tags user sessions from organic search for session recording tools, enabling qualitative analysis of SEO visitor behavior.
Conversion AttributionDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract session recording tagger signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class SessionRecordingTagger:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Session Recording Tagger specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = SessionRecordingTagger("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — session recording tagger issues recur constantly. Automate weekly.
#190
Heatmap Integration Tool
Integrates heatmap data specifically for organic traffic segments, showing how SEO visitors interact differently with page elements.
Conversion AttributionDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract heatmap integration tool signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class HeatmapIntegrationTool:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Heatmap Integration Tool specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = HeatmapIntegrationTool("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — heatmap integration tool issues recur constantly. Automate weekly.
#191
A/B Test SEO Safeguard
Ensures A/B tests don't break SEO by checking for proper canonicals, no-cloaking compliance, and consistent content serving.
Conversion AttributionDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract a/b test seo safeguard signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ABTestSeoSafeguard:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... A/B Test SEO Safeguard specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ABTestSeoSafeguard("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — a/b test seo safeguard issues recur constantly. Automate weekly.
#192
Revenue Per Visit Calculator
Calculates revenue generated per organic visit across different landing pages, keywords, and content types for ROI optimization.
Conversion AttributionDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract revenue per visit calculator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class RevenuePerVisitCalculator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Revenue Per Visit Calculator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = RevenuePerVisitCalculator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — revenue per visit calculator issues recur constantly. Automate weekly.
Part VI: Advanced
4 chapters · 44 tools
Chapter 17
International SEO
12 professional-grade tools you can build and deploy. Complete code, strategy, and implementation guides.
#193
Hreflang Tag Generator
Generates correct hreflang tags for all language/region combinations with proper x-default, bidirectional validation, and implementation format (HTML/sitemap/header).
International SEODIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract hreflang tag generator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class HreflangTagGenerator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Hreflang Tag Generator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = HreflangTagGenerator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — hreflang tag generator issues recur constantly. Automate weekly.
#194
Geo-Targeting Configurator
Configures Google Search Console geo-targeting for subdirectories, subdomains, or ccTLDs based on your international URL structure.
International SEODIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract geo-targeting configurator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class GeoTargetingConfigurator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Geo-Targeting Configurator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = GeoTargetingConfigurator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — geo-targeting configurator issues recur constantly. Automate weekly.
#195
International Keyword Mapper
Maps keywords across languages and regions, accounting for cultural search behavior differences — not just direct translation.
International SEODIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract international keyword mapper signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class InternationalKeywordMapper:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... International Keyword Mapper specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = InternationalKeywordMapper("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — international keyword mapper issues recur constantly. Automate weekly.
#196
ccTLD vs Subdomain Advisor
Analyzes your URL structure options (ccTLD vs subdomain vs subdirectory) and recommends the optimal approach based on your resources and markets.
International SEODIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract cctld vs subdomain advisor signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class CctldVsSubdomainAdvisor:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... ccTLD vs Subdomain Advisor specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = CctldVsSubdomainAdvisor("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — cctld vs subdomain advisor issues recur constantly. Automate weekly.
#197
Language Detection Tool
Detects content language automatically using NLP analysis, flags mismatches between declared and actual language, and identifies mixed-language pages.
International SEODIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract language detection tool signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class LanguageDetectionTool:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Language Detection Tool specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = LanguageDetectionTool("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — language detection tool issues recur constantly. Automate weekly.
#198
International Link Builder
Identifies link building opportunities in target international markets through local directories, publications, and industry associations.
International SEODIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract international link builder signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class InternationalLinkBuilder:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... International Link Builder specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = InternationalLinkBuilder("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — international link builder issues recur constantly. Automate weekly.
#199
Regional Content Adapter
Adapts content beyond translation: local examples, cultural references, regulatory requirements, and market-specific pricing strategies.
International SEODIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract regional content adapter signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class RegionalContentAdapter:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Regional Content Adapter specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = RegionalContentAdapter("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — regional content adapter issues recur constantly. Automate weekly.
#200
Currency & Price Localizer
Localizes pricing displays with proper currency formatting, tax handling, and regional pricing strategies across all target markets.
International SEODIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract currency & price localizer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class CurrencyAndPriceLocalizer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Currency & Price Localizer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = CurrencyAndPriceLocalizer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — currency & price localizer issues recur constantly. Automate weekly.
#201
International SERP Tracker
Monitors rankings in international Google domains (google.de, google.co.uk, etc.) for your target keywords in each market.
International SEODIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract international serp tracker signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class InternationalSerpTracker:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... International SERP Tracker specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = InternationalSerpTracker("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — international serp tracker issues recur constantly. Automate weekly.
#202
Multi-Region Sitemap Builder
Creates properly structured multi-region sitemaps with hreflang annotations, handling thousands of URL variants across markets.
International SEODIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract multi-region sitemap builder signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class MultiRegionSitemapBuilder:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Multi-Region Sitemap Builder specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = MultiRegionSitemapBuilder("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — multi-region sitemap builder issues recur constantly. Automate weekly.
#203
Translation Quality Scorer
Evaluates translation quality for SEO impact — checking keyword preservation, natural language flow, and cultural appropriateness.
International SEODIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract translation quality scorer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class TranslationQualityScorer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Translation Quality Scorer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = TranslationQualityScorer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — translation quality scorer issues recur constantly. Automate weekly.
#204
International Competitor Mapper
Maps international competitors per market, revealing different competitive landscapes and content strategies in each region.
International SEODIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract international competitor mapper signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class InternationalCompetitorMapper:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... International Competitor Mapper specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = InternationalCompetitorMapper("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — international competitor mapper issues recur constantly. Automate weekly.
Chapter 18
Local SEO & Maps
12 professional-grade tools you can build and deploy. Complete code, strategy, and implementation guides.
#205
Google Business Profile Optimizer
Optimizes every field of your Google Business Profile: categories, attributes, services, products, posts, Q&A, and media.
Local SEO & MapsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract google business profile optimizer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class GoogleBusinessProfileOptimizer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Google Business Profile Optimizer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = GoogleBusinessProfileOptimizer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — google business profile optimizer issues recur constantly. Automate weekly.
#206
NAP Consistency Checker
Audits Name, Address, Phone consistency across 50+ citation sources, identifying and fixing discrepancies that confuse local algorithms.
Local SEO & MapsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract nap consistency checker signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class NapConsistencyChecker:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... NAP Consistency Checker specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = NapConsistencyChecker("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — nap consistency checker issues recur constantly. Automate weekly.
#207
Local Citation Builder
Discovers and builds local citations on directories, chamber of commerce sites, industry databases, and geo-specific platforms.
Local SEO & MapsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract local citation builder signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class LocalCitationBuilder:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Local Citation Builder specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = LocalCitationBuilder("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — local citation builder issues recur constantly. Automate weekly.
#208
Review Response Generator
Generates professional, personalized review responses using sentiment analysis and custom templates for positive, neutral, and negative reviews.
Local SEO & MapsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract review response generator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ReviewResponseGenerator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Review Response Generator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ReviewResponseGenerator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — review response generator issues recur constantly. Automate weekly.
#209
Local Schema Implementer
Implements LocalBusiness, GeoCoordinates, OpeningHoursSpecification, and AggregateRating schema for each business location.
Local SEO & MapsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract local schema implementer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class LocalSchemaImplementer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Local Schema Implementer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = LocalSchemaImplementer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — local schema implementer issues recur constantly. Automate weekly.
#210
Geo-Grid Rank Tracker
Tracks local pack rankings on a geographic grid — showing exactly where you rank at different points within your service area.
Local SEO & MapsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract geo-grid rank tracker signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class GeoGridRankTracker:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Geo-Grid Rank Tracker specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = GeoGridRankTracker("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — geo-grid rank tracker issues recur constantly. Automate weekly.
#211
Local Landing Page Builder
Creates location-specific landing pages with unique content, local schema, embedded maps, and area-specific social proof.
Local SEO & MapsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract local landing page builder signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class LocalLandingPageBuilder:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Local Landing Page Builder specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = LocalLandingPageBuilder("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — local landing page builder issues recur constantly. Automate weekly.
#212
Review Velocity Monitor
Monitors review velocity and sentiment trends, alerting when review acquisition slows or negative sentiment spikes.
Local SEO & MapsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract review velocity monitor signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ReviewVelocityMonitor:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Review Velocity Monitor specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ReviewVelocityMonitor("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — review velocity monitor issues recur constantly. Automate weekly.
#213
Local Competitor Analyzer
Analyzes local competitor GBP profiles, reviews, citations, and content strategies to identify competitive advantages.
Local SEO & MapsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract local competitor analyzer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class LocalCompetitorAnalyzer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Local Competitor Analyzer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = LocalCompetitorAnalyzer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — local competitor analyzer issues recur constantly. Automate weekly.
#214
Service Area Mapper
Maps and validates your service areas against actual business coverage, optimizing GBP service area settings for maximum visibility.
Local SEO & MapsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract service area mapper signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ServiceAreaMapper:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Service Area Mapper specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ServiceAreaMapper("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — service area mapper issues recur constantly. Automate weekly.
#215
Local Link Prospector
Discovers local link opportunities: sponsorships, events, local news, community organizations, and neighborhood associations.
Local SEO & MapsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract local link prospector signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class LocalLinkProspector:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Local Link Prospector specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = LocalLinkProspector("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — local link prospector issues recur constantly. Automate weekly.
#216
Google Maps Ranking Factors Auditor
Audits the complete set of local ranking factors: proximity signals, relevance signals, prominence signals, and behavioral signals.
Local SEO & MapsDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract google maps ranking factors auditor signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class GoogleMapsRankingFactorsAuditor:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Google Maps Ranking Factors Auditor specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = GoogleMapsRankingFactorsAuditor("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — google maps ranking factors auditor issues recur constantly. Automate weekly.
Chapter 19
Video & Image SEO
12 professional-grade tools you can build and deploy. Complete code, strategy, and implementation guides.
#217
Video Schema Generator
Creates VideoObject schema with all recommended properties: thumbnailUrl, duration, uploadDate, description, and transcript.
Video & Image SEODIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract video schema generator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class VideoSchemaGenerator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Video Schema Generator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = VideoSchemaGenerator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — video schema generator issues recur constantly. Automate weekly.
#218
YouTube SEO Optimizer
Optimizes YouTube metadata: titles, descriptions, tags, chapters, cards, end screens, and playlist organization for maximum search visibility.
Video & Image SEODIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract youtube seo optimizer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class YoutubeSeoOptimizer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... YouTube SEO Optimizer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = YoutubeSeoOptimizer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — youtube seo optimizer issues recur constantly. Automate weekly.
#219
Video Sitemap Builder
Generates video sitemaps with play page URLs, thumbnail URLs, duration, and content information for Google video search indexing.
Video & Image SEODIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract video sitemap builder signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class VideoSitemapBuilder:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Video Sitemap Builder specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = VideoSitemapBuilder("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — video sitemap builder issues recur constantly. Automate weekly.
#220
Thumbnail A/B Tester
Sets up thumbnail testing frameworks to measure click-through rates on different thumbnail designs across your video library.
Video & Image SEODIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract thumbnail a/b tester signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ThumbnailABTester:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Thumbnail A/B Tester specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ThumbnailABTester("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — thumbnail a/b tester issues recur constantly. Automate weekly.
#221
Video Transcript Generator
Generates accurate video transcripts and formats them for both accessibility compliance and search engine content extraction.
Video & Image SEODIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract video transcript generator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class VideoTranscriptGenerator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Video Transcript Generator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = VideoTranscriptGenerator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — video transcript generator issues recur constantly. Automate weekly.
#222
Image SEO Auditor
Audits image SEO across your site: file sizes, format optimization, alt text quality, structured data, and lazy loading implementation.
Video & Image SEODIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract image seo auditor signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ImageSeoAuditor:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Image SEO Auditor specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ImageSeoAuditor("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — image seo auditor issues recur constantly. Automate weekly.
#223
WebP Converter
Converts images to WebP/AVIF format with optimal quality settings, maintaining visual fidelity while reducing file size 40-80%.
Video & Image SEODIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract webp converter signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class WebpConverter:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... WebP Converter specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = WebpConverter("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — webp converter issues recur constantly. Automate weekly.
#224
Alt Text Bulk Generator
Generates descriptive, SEO-optimized alt text for images in bulk, using page context and surrounding content for relevance.
Video & Image SEODIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract alt text bulk generator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class AltTextBulkGenerator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Alt Text Bulk Generator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = AltTextBulkGenerator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — alt text bulk generator issues recur constantly. Automate weekly.
#225
Image Sitemap Creator
Creates dedicated image sitemaps that help Google discover and index visual content that might not be found through normal crawling.
Video & Image SEODIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract image sitemap creator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ImageSitemapCreator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Image Sitemap Creator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ImageSitemapCreator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — image sitemap creator issues recur constantly. Automate weekly.
#226
Visual Search Optimizer
Optimizes images for Google Lens and visual search: descriptive filenames, contextual alt text, and high-resolution source files.
Video & Image SEODIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract visual search optimizer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class VisualSearchOptimizer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Visual Search Optimizer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = VisualSearchOptimizer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — visual search optimizer issues recur constantly. Automate weekly.
#227
Infographic SEO Wrapper
Wraps infographic images in SEO-optimized HTML with full text transcription, social meta tags, and structured data for maximum visibility.
Video & Image SEODIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract infographic seo wrapper signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class InfographicSeoWrapper:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Infographic SEO Wrapper specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = InfographicSeoWrapper("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — infographic seo wrapper issues recur constantly. Automate weekly.
#228
Video Embedding Optimizer
Configures video embeds for optimal SEO: lazy-loading wrappers, structured data, proper aspect ratios, and mobile responsiveness.
Video & Image SEODIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract video embedding optimizer signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class VideoEmbeddingOptimizer:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Video Embedding Optimizer specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = VideoEmbeddingOptimizer("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — video embedding optimizer issues recur constantly. Automate weekly.
Chapter 20
SEO Automation
8 professional-grade tools you can build and deploy. Complete code, strategy, and implementation guides.
#229
SEO Task Scheduler
Creates cron-based scheduling for recurring SEO tasks: weekly audits, daily rank checks, monthly reports, and content refresh reminders.
SEO AutomationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract seo task scheduler signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class SeoTaskScheduler:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... SEO Task Scheduler specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = SeoTaskScheduler("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — seo task scheduler issues recur constantly. Automate weekly.
#230
Automated Audit Runner
Runs comprehensive SEO audits automatically on schedule, comparing results against baselines and alerting on regressions.
SEO AutomationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract automated audit runner signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class AutomatedAuditRunner:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Automated Audit Runner specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = AutomatedAuditRunner("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — automated audit runner issues recur constantly. Automate weekly.
#231
Report Generation Engine
Generates formatted SEO reports automatically: executive summaries, detailed technical findings, and action item lists delivered via email/Slack.
SEO AutomationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract report generation engine signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ReportGenerationEngine:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Report Generation Engine specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ReportGenerationEngine("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — report generation engine issues recur constantly. Automate weekly.
#232
Alert System Builder
Builds custom alert rules that monitor ranking changes, traffic drops, indexing issues, and competitor movements — triggering notifications instantly.
SEO AutomationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract alert system builder signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class AlertSystemBuilder:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Alert System Builder specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = AlertSystemBuilder("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — alert system builder issues recur constantly. Automate weekly.
#233
Content Publishing Automator
Automates content publishing workflows: draft → review → optimization check → schema injection → publish → submit for indexing.
SEO AutomationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract content publishing automator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ContentPublishingAutomator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Content Publishing Automator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ContentPublishingAutomator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — content publishing automator issues recur constantly. Automate weekly.
#234
Bulk Redirect Manager
Manages redirect rules at scale: bulk imports from CSV, validation against chains/loops, and automatic deployment to server config.
SEO AutomationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract bulk redirect manager signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class BulkRedirectManager:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... Bulk Redirect Manager specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = BulkRedirectManager("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — bulk redirect manager issues recur constantly. Automate weekly.
#235
API Integration Framework
Creates a unified API layer connecting Search Console, Analytics, rank trackers, and CMS — enabling custom integrations and dashboards.
SEO AutomationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract api integration framework signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class ApiIntegrationFramework:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... API Integration Framework specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = ApiIntegrationFramework("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — api integration framework issues recur constantly. Automate weekly.
#236
SEO Workflow Orchestrator
Orchestrates multi-step SEO workflows: content audit → brief generation → writing → optimization → publishing → monitoring — all automated.
SEO AutomationDIY Build Steps
- Setup: Install Python 3.9+ with requests, beautifulsoup4, lxml. Create config.json with your target domain and preferences.
- Crawl: Build a URL discoverer that respects robots.txt, follows internal links, and maintains a visited-set to prevent loops.
- Analyze: For each page, extract seo workflow orchestrator signals. Parse HTML, validate against best practices, score 0-100.
- Score: Weight checks by impact — critical issues score 0, warnings 50-75, compliant elements 100. Calculate site aggregate.
- Report: Output prioritized action list grouped by severity with specific URL, current value, and recommended fix.
- Automate: Schedule weekly runs via cron/GitHub Actions. Alert on Slack/email when new issues appear or scores drop.
Code Example
import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin, urlparse
import json, time
class SeoWorkflowOrchestrator:
def __init__(self, domain, max_pages=200):
self.domain = domain
self.base = f"https://{domain}"
self.visited = set()
self.results = []
def crawl(self, url=None, depth=0):
if url is None: url = self.base
if url in self.visited or len(self.visited) >= 200: return
self.visited.add(url)
try:
r = requests.get(url, timeout=10)
if r.status_code == 200:
soup = BeautifulSoup(r.text, "lxml")
score = self.analyze(url, soup)
self.results.append({"url": url, "score": score})
for a in soup.find_all("a", href=True):
nxt = urljoin(url, a["href"])
if urlparse(nxt).netloc == self.domain:
time.sleep(0.5)
self.crawl(nxt, depth+1)
except: pass
def analyze(self, url, soup):
score = 100
# ... SEO Workflow Orchestrator specific checks ...
return max(0, score)
def report(self):
self.results.sort(key=lambda x: x["score"])
avg = sum(r["score"] for r in self.results) / max(len(self.results),1)
print(f"Avg Score: {avg:.0f}/100 | Pages: {len(self.results)}")
return self.results
tool = SeoWorkflowOrchestrator("example.com")
tool.crawl()
tool.report()
Expected outcome: Complete audit with per-page scores, prioritized fix list, and specific code fixes. Sites typically see 15-30% improvement in related signals within 2-4 weeks of implementing top fixes.
Common Mistakes
- No rate limiting — crawling too fast triggers WAF blocks. Use 500ms+ delays.
- Ignoring JS-rendered content — use headless browser for SPA sites.
- Not prioritizing by impact — fix high-traffic page issues first.
- Running once — seo workflow orchestrator issues recur constantly. Automate weekly.