AI Readiness Audit Skill

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.

Quick Start

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"

Workflow

Step 1: Get the Target URL

Ask the user which website to audit. Accept any valid URL.

Step 2: Run the Audit

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 grade
categories — per-category scores and findings
recommendations — actionable fixes sorted by priority
boostScore — projected score after applying Inlay Boost (if available)

Step 3: Present the Report

Format the results as a clear report. See examples/sample-report.md for the expected format.

Report structure:

Header — Site URL, overall score, letter grade
Grade Scale — A+ (90-100), A (80-89), B (70-79), C (60-69), D (40-59), F (0-39)
Category Breakdown — Table with each category's score and status
Top Issues — Negative findings that hurt the score
Recommendations — Actionable fixes sorted by impact (high → low)
Inlay Boost — Projected score if Inlay Boost data is available

Step 4: Offer to Fix Issues

After presenting the report, offer to fix issues automatically:

llms.txt missing → Use the setup-llms-txt skill to create one
No MCP server → Use the setup-mcp-server skill to set one up
Missing structured data → Generate JSON-LD schema markup
Poor meta tags → Rewrite title/description for AI discoverability
Missing robots.txt directives → Add AI bot permissions
No sitemap → Generate or update sitemap.xml

For each fixable issue, explain what it is, why it matters for AI agents, and offer to implement the fix in the user's codebase.

Categories Reference

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

Common Fixes

See references/fixes.md for detailed fix instructions for each category.

Tips

Run audits on both the homepage and key inner pages
Compare scores before/after implementing fixes
Focus on high-weight categories first for maximum impact
The Inlay Boost projected score shows the potential improvement from using Inlay's tools

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