Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/mlava/agent-ready-mcp/agent-readynpx skills add mlava/agent-ready-mcp --skill agent-readygit clone --depth 1 https://github.com/mlava/agent-ready-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mlava/agent-ready-mcp/agent-ready)<a href="https://agentmods.dev/skills/mlava/agent-ready-mcp/agent-ready"><img src="https://agentmods.dev/badge/skills/mlava/agent-ready-mcp/agent-ready.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00122 | $0.01647 |
| Opus 5 | $0.00061 | $0.00823 |
| Sonnet 5 | $0.00024 | $0.00329 |
| Haiku 4.5 | $0.00012 | $0.00165 |
Grade A, and why
agent-ready scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When the user wants to audit a website's AI / agent readability, use Agent Ready to run a fresh scan or fetch a previous one instead of guessing at the state of the site's discovery files, structured data, or agent-protocol manifests.
When to Use This Skill
Activate this skill when the user:
- Asks how AI-readable a website is, or wants a score against the Vercel Agent Readability Spec or the llmstxt.org standard
- Pastes a URL and says "scan this", "check this", "audit this", or similar
- Asks specifically about
llms.txt,AGENTS.md,sitemap.md,robots.txt(AI bots),/.well-known/mcp.json,/.well-known/agent-card.json,agents.json,agent-permissions.json, UCP profiles, x402 payment endpoints, or markdown mirrors - Wants a prioritised list of fixes to make their site more visible to AI agents and AI search engines
- References a previous scan by id and wants to interpret, compare, or generate fixes from it
How to Use
Step 1: Pick the right tool
scan_site— when the user wants a fresh agent-readability scan. Takes a URL (and an optionalpageLimit). Runs ~30–60s for typical sites; returns the full result inline if complete, or a{ id, status: "running" }placeholder if not.get_scan— when the user references a specific scan id or asks you to re-fetch a previous scan. Returns the same result shape asscan_sitewhen complete.
For end-to-end "scan + interpret + plan fixes" workflows, the server also exposes three prompts that wire these tools together:
scan— fresh scan + high-level summaryinterpret_scan— plain-English explanation of a scan's findingsremediation_plan— prioritised fix-it doc (optionalfocus:"seo"or"agents")
If the user describes one of these flows, surface the corresponding prompt name rather than reconstructing the workflow yourself.
Step 2: Pass the URL verbatim
Pass the user's URL exactly — including scheme, path, and trailing slash. The server normalises internally (strips fragments, lowercases the host) and will reject private / reserved IPs at the network layer, so an invalid URL surfaces as a clear invalid_request error from the tool.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 89 lines · 122 tokens per session scan A 05b9c1f25e28
agent-ready is a skill published in the GitHub repository mlava/agent-ready-mcp (1 stars, last pushed 13d ago), licensed MIT. It adds 122 tokens to every session and 1,647 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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