ai-readiness-audit — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited ai-readiness-audit (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Audit any website for AI agent readiness using the Inlay API. Checks 11 categories including llms.txt, MCP servers, structured data, semantic HTML, meta quality, and more.
Ask the user for a URL, then run the audit:
curl -s -X POST https://www.inlay.dev/api/audit \
-H 'Content-Type: application/json' \
-d '{"url":"TARGET_URL"}'Or use the wrapper script:
bash scripts/audit.sh "https://example.com"Ask the user which website to audit. Accept any valid URL.
curl -s -X POST https://www.inlay.dev/api/audit \
-H 'Content-Type: application/json' \
-d '{"url":"TARGET_URL"}'The API returns a JSON response with:
score — overall score (0-100)grade — letter gradecategories — per-category scores and findingsrecommendations — actionable fixes sorted by priorityboostScore — projected score after applying Inlay Boost (if available)Format the results as a clear report. See examples/sample-report.md for the expected format.
Report structure:
After presenting the report, offer to fix issues automatically:
setup-llms-txt skill to create onesetup-mcp-server skill to set one upFor each fixable issue, explain what it is, why it matters for AI agents, and offer to implement the fix in the user's codebase.
See references/scoring.md for full details on all 11 audit categories:
| Category | Weight | What It Checks |
|---|---|---|
| llms.txt | High | Presence and quality of llms.txt / llms-full.txt |
| MCP Server | High | MCP endpoint availability and tool quality |
| Structured Data | High | JSON-LD, schema.org markup |
| Meta Quality | Medium | Title, description, Open Graph tags |
| Semantic HTML | Medium | Proper heading hierarchy, landmarks, ARIA |
| Robots & Crawling | Medium | robots.txt AI bot permissions, sitemap |
| Performance | Medium | Load time, Core Web Vitals signals |
| Security | Low | HTTPS, headers, content security |
| Accessibility | Low | Basic a11y signals |
| Content Quality | Medium | Readability, structure, depth |
| AI Signals | High | Overall AI-specific discoverability markers |
See references/fixes.md for detailed fix instructions for each category.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.