The problem
Technical debt silently erodes rankings between audits
Most sites are audited quarterly, if at all. Between audits, a deploy breaks canonical tags, an image bloats LCP, a redirect chain forms, or a schema change invalidates rich results — and traffic slides before anyone notices.
This solution crawls continuously, triages what matters, and explains each fix in plain language — so issues are caught in hours, not quarters.
Primary objective
Keep the site technically healthy on autopilot, with fixes ready to ship.
How it works
Crawl → detect → prioritise → fix & verify
Crawl
Scheduled full & delta crawls + Lighthouse
→
Detect
Rules + model reasoning over crawl data
→
Prioritise
Impact × effort → ranked backlog
→
Fix & verify
Ticket + patch, re-crawl to confirm
Checks it runs
- ✓ Broken links & redirect chains
- ✓ Core Web Vitals (LCP, CLS, INP)
- ✓ Indexation & canonicalisation
- ✓ Duplicate / thin content
- ✓ Structured-data validation
- ✓ Sitemap & robots hygiene
Built with
- ✓ Headless crawler + Lighthouse
- ✓ Claude for triage & fix drafting
- ✓ Search Console integration
- ✓ Jira / Linear / GitHub tickets
- ✓ Slack & email alerting
Every issue ships with
- ✓ Plain-language explanation
- ✓ Affected URLs list
- ✓ Estimated ranking impact
- ✓ Concrete fix / code snippet
- ✓ Re-check after deploy
Under the hood
From crawl signal to a fixable ticket
Rules flag anomalies; the model turns each one into a prioritised, human-readable ticket with a fix — never a raw error dump.
audit/triage.py
PYTHON
from anthropic import Anthropic client = Anthropic() def to_ticket(issue: dict) -> dict: # issue = {type, urls, metrics, snippet} from the crawler prompt = f"""You are a technical SEO engineer. Issue: {issue['type']} Affected URLs: {issue['urls'][:20]} Signals: {issue['metrics']} Write a ticket as JSON with: title, severity (low|med|high), estimated_impact, root_cause, fix (concrete steps or code), verification (how to confirm it's resolved).""" msg = client.messages.create( model="claude-sonnet-4-20250514", max_tokens=900, temperature=0, messages=[{"role": "user", "content": prompt}], ) ticket = parse_json(msg.content[0].text) ticket["score"] = impact_over_effort(ticket) # drives backlog order return ticket
Outcomes
24/7
Continuous monitoring
Hours
To catch regressions
Ready
Fixes, not just findings
Ranked
Backlog by impact
Stop losing rankings to silent bugs
Point it at your site and get a prioritised, fixable backlog this week.
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