Self-hosted MIT-licensed SEO crawler and auditor: crawls websites, surfaces technical SEO issues, broken links, missing metadata, and duplicate content.
scanned 16d ago·osv · gitleaks · opengrep · picklescan + heuristics·full breakdown in the Security tab ↓
SEOnaut — Open-Source SEO Audit Tool
SEOnaut is an open-source, self-hosted SEO audit tool written in Go. It crawls websites and generates comprehensive technical SEO reports — surfacing broken links (4xx/5xx), missing title tags and meta descriptions, duplicate content signals, redirect chains, image alt text issues, canonical tag problems, and structured data validation. A clean dashboard makes findings actionable for marketing and development teams.
Key Features
Full website crawl with configurable depth and concurrency
Detects: broken links, missing/duplicate titles and meta descriptions, redirect chains, missing alt text, hreflang errors
JSON + CSV export for integration with reporting pipelines
Lightweight Go binary — runs on a $5 VPS alongside your app
Quick Start
# Docker deployment
docker run -p 8080:8080 stJudeWasHere/seonaut
# Or build from source
git clone https://github.com/StJudeWasHere/seonaut
cd seonaut && go build ./...
./seonaut --addr :8080
Then open http://localhost:8080, add your website URL, and run the crawler. Results appear in a dashboard with filterable issue categories.
npx ai-supply add seonaut-seo-audit-tool
Curated mirror of the open-source SEOnaut (MIT). Get it from the source.
! Security: Review · 7575/100 · grade Bscanned 16d ago
✓ no compromise signals13 risk-surface · 7/20 OWASP controls flagged
Compromise signals — malicious or tampered code (leaked secrets, backdoors, a dropped executable) — reduce the score, and known dependency CVEs carry a bounded penalty (they warrant review but never QUARANTINE — update the dependency to clear). Other dangerous-by-capability traits are risk surface, expected for some capabilities. Every finding is mapped to its OWASP control below.
What this capability can do · med confidence (static)
Findings mapped to the OWASP Top 10 for LLM Applications (2025) and the OWASP Machine Learning Security Top 10. Expand any flagged control for the exact findings — compromise reduces the score; expected/risk-surface do not, except a known CVE, which carries a small bounded penalty (high/critical → Review).
OWASP Top 10 for LLM Applications
⚠LLM03Supply Chaincritical
Vulnerable/compromised dependencies, models or archives in the artifact.