Server logs are the only record of what Googlebot actually does. Here’s a runnable Python pipeline to verify Googlebot, measure crawl-budget waste, and find the pages Google ignores.
A teardown of Screaming Frog, Sitebulb, and JetOctopus for SEO engineers who want to run crawls headlessly on a schedule — with a working Screaming Frog CLI automation recipe.
Build a scheduled Python pipeline that reconciles your internal link graph, XML sitemap, and Search Console URLs to surface orphan pages, near-orphans, and crawl-budget leaks.
Pipe Google Search Console’s Bulk Data Export into BigQuery and a self-updating Looker Studio dashboard. Full architecture, SQL views with correct weighted position, a 16-month backfill script, and cost controls.
Build a Python guardrail that monitors robots.txt, meta robots, and the X-Robots-Tag header against a baseline, and alerts the moment a deploy accidentally noindexes your money pages.
A working Python pipeline that pulls GA4 organic sessions, strips out weekly seasonality with STL, scores residuals with robust statistics, and pings Slack the moment an organic traffic drop becomes statistically real.
Link rot is a steady drip, not a one-time event. Build a continuous Python pipeline that crawls, reconciles GSC and server logs, classifies broken links and soft 404s, and suggests redirect targets with embeddings.
We rebuilt the same SEO workflow on n8n, Make, and Zapier to compare cost, control, and scaling. Here is which platform wins and when.
A content refresh pipeline is the action layer on top of decay detection: score decaying pages by recoverable opportunity, diff them against SERP consensus, and generate specific LLM update briefs.
A code-first Python + n8n pipeline that tracks whether ChatGPT, Perplexity, and Google AI Overviews cite your site, so you can trend AI visibility like keyword rankings.