SKILL.md
Agent Search Optimisation
Quick start
When given a website URL, produce an evidence-based optimisation plan for AI agents and AI search.
- Crawl the public site surfaces:
- homepage - robots.txt - sitemap.xml and sitemap index - llms.txt / llms-full.txt if present - key navigation pages - representative article, product, project, docs, pricing, and about pages
- Audit agent-readiness:
- crawlability and renderability - canonical URLs - sitemap coverage - structured data - language alternates - entity clarity - answer-oriented page summaries - machine-readable indexes or APIs - internal linking and topic hubs
- Research current AI search best practices before making claims about current platforms.
- Create a prioritized roadmap with:
- quick wins - technical fixes - content changes - agent-facing data surfaces - measurement plan
Workflow
1. Clarify the goal
Infer the likely goal from the user request. Ask only when necessary.
Common goals:
- increase AI answer citations
- make agents understand a product or portfolio
- expose documentation to coding agents
- improve local/business discovery in AI search
- prepare content for retrieval-augmented systems
- control AI crawler access
2. Collect evidence
Check these URLs where applicable:
{site}/
{site}/robots.txt
{site}/sitemap.xml
{site}/llms.txt
{site}/llms-full.txt
{site}/.well-known/Also inspect at least 5 representative pages when the site has enough content:
- homepage
- about/company/profile page
- main collection/archive page
- one detail page
- one recent article/docs page
3. Score the site
Use a 0-3 score for each area:
| Area | 0 | 1 | 2 | 3 |
|---|---|---|---|---|
| Crawlability | blocked/broken | partially crawlable | mostly crawlable | clean HTML + clear policy |
| Discovery | no sitemap | partial sitemap | complete sitemap | sitemap index + freshness |
| Structured data | none | basic metadata | JSON-LD on some templates | complete schema graph |
| Entity clarity | vague | some entities | clear entities | entity graph + IDs |
| Content extractability | thin/visual | prose only | summaries present | answer blocks + JSON |
| Language/canonicals | absent | inconsistent | mostly correct | canonical + hreflang complete |
| Agent surface | none | llms.txt only | index/feed | API/search/content endpoints |
| Measurement | none | traffic only | search console | AI/retrieval benchmark |
4. Recommend changes
Prioritize in this order unless the site context suggests otherwise:
- Fix public crawl/discovery basics.
- Add canonical URLs, metadata, and language alternates.
- Add JSON-LD and entity IDs.
- Add answer-oriented summaries to important pages.
- Add topic hubs and internal links.
- Add llms.txt as an orientation layer.
- Add machine-readable content index.
- Add semantic search/API only when the corpus is large enough.
- Add measurement and recurring evaluation.
5. Deliver the plan
Structure the final answer as:
- Executive summary
- What I checked
- Current strengths
- Gaps and risks
- Prioritized roadmap
- Implementation details
- Measurement plan
- Open questions / assumptions
Output rules
- Do not claim a file or feature exists unless verified.
- Mark unverified items clearly.
- Prefer durable web standards over hype.
- Treat
llms.txtas additive, not a replacement for HTML, sitemaps, metadata, or structured data. - Separate discovery from access control; robots.txt is not security.
- Include concrete examples when possible.
- Keep recommendations implementation-ready.
- Use current web research for AI search platform behavior, crawler policies, and new conventions.
- If the user provides a private repo or codebase, inspect implementation before suggesting exact code changes.
Advanced features
See [REFERENCE.md](REFERENCE.md) for audit criteria, schema recommendations, llms.txt guidance, roadmap templates, and API examples.
See [EXAMPLES.md](EXAMPLES.md) for output examples and reusable prompts.
Use [scripts/audit-agent-readiness.mjs](scripts/audit-agent-readiness.mjs) for a lightweight first-pass technical audit.