agent-ready
Skill1 starsChanged 2 months agoMITView on GitHub
Audit and fix how ready a website is for AI agents - robots.txt, Content Signals, markdown negotiation, llms.txt, MCP server cards, agent skills, ARD/ai-catalog.json, WebMCP, OAuth discovery, auth.md, A2A cards and agentic commerce. Use when asked whether a site is agent-ready, AI-ready or LLM-ready, to score it 0-5, to fix a failing check, or to generate the .well-known files a site needs for agents.
10 other projects use a version of this.
Adopted by
Most recently taken up by
- afaraha8403/balakittheir own version, first seen Nov 6, 2026
- dgalarza/agent-skillstheir own version, first seen Nov 1, 2026
- howardchenghuang-dotcom/agent-readytheir own version, first seen Nov 1, 2026
- ora/skillstheir own version, first seen Sep 28, 2026
- mblode/agent-skillstheir own version, first seen Sep 11, 2026
- DamonRabie/agents_librarytheir own version, first seen Aug 22, 2026
Other versions10 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 10
- mblode/agent-skills★ 189same name
- dgalarza/agent-skills★ 72same name
- Owl-Listener/agent-ready★ 10same name
- afaraha8403/balakit★ 1same name
- DamonRabie/agents_library★ 1same name
- hao0401/agent-ready★ 1same name
- ora/skills★ 1same name
- duongxthanh/agent-ready★ 0same name
- howardchenghuang-dotcom/agent-ready★ 0same name
- shuvoships/agent-ready★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repositoryOne other file it gives its agents.
The file
We split this skill into the rules it gives an agent
6 rules. 0 are common, 4 are distinctive, and 2 are very specific to this project.
0
Common
Most of the 9 edited versions we read keep these.
4
Distinctive
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 25
Run the scanner. It is stdlib-only Python 3.8+, no install step.
- Distinctive: line 55
Do not offer to fix things the user has not asked about yet.
- Very specific: line 62
Read references/levels.md before proposing an order of work.
- Very specific: line 67
Read its entry in references/checks.md — probe, pass condition, trap.
- Distinctive: line 72
Put it where the site actually serves static files.
- Distinctive: line 106
Never pick a default quietly on these.
“Common” is measured: kept by at least 6 in 10 of the 9 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Agent readiness: audit and fix
Scores a site 0–5 on the standards AI agents use to find, read and act on it, then generates the exact files needed to move it up. Reproduces the 22 checks Cloudflare's isitagentready.com runs, plus 8 extended checks it does not.
Decide what the user is asking for
| Ask | Do |
| "is my site agent-ready / AI-ready", a bare URL, "score my site" | Audit |
| "fix X", "make it level 4", "generate the files" | Fix — audit first if you have not |
| "what is llms.txt / ARD / Content Signals", "should I …" | Answer from references/checks.md; do not scan |
| "review my robots.txt / ai-catalog.json" (file in the repo) | Read the file, validate against references/checks.md |
Audit
Run the scanner. It is stdlib-only Python 3.8+, no install step.
python skills/agent-ready/scripts/agent_ready_scan.py <url> \
--markdown agent-readiness.md --json agent-readiness.jsonUseful flags:
--deep— also fetch same-origin scripts when looking for WebMCP. Use it before telling someone their WebMCP is missing.--remote— additionally run the official isitagentready.com scan and print both levels. Use it when the user disputes a result, or to confirm parity.--no-extended— only the 22 official checks.-v— per-check evidence (every URL probed and what came back).--only discoverability,discovery— narrow the printed report.
The scanner never raises: unreachable hosts and crashed checks come back as
err rows.
Reporting the result
Lead with the level and the single next thing. Then the fails grouped by category, then the extended track. Keep it short — the JSON has the detail.
State the level as a floor, not a grade: level 2 with a strong extended track
is a better site for agents than level 4 with an empty homepage. When a check
is n/a, say so rather than counting it as a failure — dnsAid, webBotAuth
and the commerce checks are not gaps for most sites.
Do not offer to fix things the user has not asked about yet. Give the level, the next rung, and stop.
Fix
Read references/levels.md before proposing an order of work. The ladder only
moves in sequence, but effort should be spent by impact.
For each check to fix:
- Read its entry in
references/checks.md— probe, pass condition, trap. - Copy the matching template from
assets/, replace every{{PLACEHOLDER}}with real values from the site. Never ship a file with a placeholder left in it — a malformedai-catalog.jsonfails the same check it was meant to pass. - Put it where the site actually serves static files. Find that out; do not assume a docroot.
- Set the content type.
references/checks.mdandassets/snippets/serving-recipes.md§3 list every one — this is the most common way a correct file still fails. - Re-run the scanner against the deployed URL. A file in the repo proves nothing.
What is in assets/
| File | Fixes |
robots.txt | robotsTxt, robotsTxtAiRules, contentSignals, sitemap reference, ARD Agentmap |
llms.txt | llmsTxt |
auth.md | authMd |
well-known/ai-catalog.json | ard |
well-known/mcp.json | mcpServerCard |
well-known/agent-card.json | a2aAgentCard |
well-known/api-catalog.json | apiCatalog (serve at /.well-known/api-catalog, no extension) |
well-known/agent-skills/index.json | agentSkills |
well-known/security.txt | securityTxt |
snippets/serving-recipes.md | markdownNegotiation, linkHeaders, content types |
snippets/webmcp.js | webMcp |
snippets/head-and-jsonld.html | structuredData, htmlSemantics |
Judgement calls to raise, not decide
Four questions in every audit encode a policy or product choice the site owner
owns: the ai-train Content Signal, which level to target, where the markdown
comes from, and which commerce protocol (if any). Read
references/site-decisions.md before raising any of them — it has the
trade-offs, the recommendation for each site type, and how to present them
without turning a personal blog into a decision matrix.
Never pick a default quietly on these. They end up in public, machine-readable files that state a position on the owner's behalf.
Things that are true and easy to get wrong
- Markdown negotiation without
Vary: Acceptis a bug, not a partial win. A CDN will cache one representation and serve it to the wrong client. - A named
User-agent:group replaces the wildcard group, it does not add to it. AddingUser-agent: GPTBotwith oneDisallowsilently drops yourContent-Signalfor GPTBot. /.well-known/api-cataloghas no file extension and must beapplication/linkset+json.- A site that markdown-negotiates every path returns 200 for
/anything.md. The scanner detects this soft-404 pattern; a hand check withcurlwill not. llms.txthas no search-engine weight. Google ignores it. Its value is curation for agents that choose to read it — publish it for that reason or not at all.- An MCP server card pointing at a dead endpoint is worse than no card.
- If
serverRenderedfails, fix that first. Most crawling agents never run your JavaScript, so every check above it is scoring an empty page.
References
references/checks.md— all 30 checks: probe, pass condition, fix, spec link.references/levels.md— the ladder, its validation, a prioritised backlog, and the sensible ceiling per site type.references/site-decisions.md— the four owner decisions and how to raise them.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

