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Agent Ready

Community
mblode
agent-ready

Implements agent-readiness on public sites and docs from Mintlify Agent Score, AFDocs, Is Agentic, and Is It Agent Ready reports. Covers llms.txt, markdown negotiation, OpenAPI errors, rate-limit headers, MCP discovery, and robots Link headers. Use when asked to "make this agent-ready", "improve Agent Score", "fix llms.txt coverage", or when a pasted isitagentready, is-agentic, or mintlify/score report is the brief. For docs prose use docs-writing; for CLI/SDK ergonomics use dx-audit; for agentic product UX use ax-audit; for crawler policy and Next.js markdown routes use seo.

Overview

Publishermblode
Repositoryagent-skills
Skill nameagent-ready
Stars
118
Forks
11
Bundled files
5
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by mblode on GitHub. Read the source before you install it.

Installation

Install the Agent Ready AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/mblode/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/skills/agent-ready .claude/skills/agent-ready
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Ready in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Agent Ready on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Agent Ready is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Agent Ready

Turn a public agent-readiness score into shipped, verified HTTP and docs changes.

  • IS: ingesting Mintlify Agent Score / AFDocs, Is Agentic, or Is It Agent Ready findings, inspecting the repo, implementing the matching protocols, adding tests, and verifying live responses.
  • IS NOT: rewriting docs prose (docs-writing), package/CLI/SDK ergonomics (dx-audit), whether an in-product agent can be trusted (ax-audit), or search ranking, crawler policy, and Next.js llms.txt/markdown routes (seo). Do not vendor vercel-labs/is-agentic; that skill retrieves reports. This one implements the product.

Contents

Workflow

text
Agent-ready progress:
- [ ] Step 1: Ingest the report (or run scanners against the public URL)
- [ ] Step 2: Inspect the existing codebase before any edit
- [ ] Step 3: Map each finding to a real surface; skip surfaces the product does not offer
- [ ] Step 4: Load the matching reference and implement failures, then warnings. For Next.js App Router `llms.txt` and markdown routes, load `seo` (that skill's `nextjs-implementation.md` and `answer-engines.md`) instead of a second recipe.
- [ ] Step 5: Add or update tests for every behavior you change
- [ ] Step 6: Verify every public endpoint and machine-readable file you touched
- [ ] Step 7: Report changes, quoted verification, and remaining product decisions

A pasted scorecard is the spec. If none is present and the user named a public URL, gather one:

bash
npx afdocs check <docs-url> --format scorecard --sampling deterministic
npx is-agentic <domain> --json

Is It Agent Ready: POST https://isitagentready.com/api/scan with {"url":"<origin>","format":"agent"}. Prefer the user's pasted report over a new scan when both exist.

Preserve visual design and existing product behavior. Change discovery, representations, headers, and documented contracts, not the feature set.

Local test suites that cannot reach production are safe to run, fix, and rerun. Do not deploy, change DNS, buy a registry name, or write outside the working tree without authorization.

Done when every in-scope failing check has a code or content change (or an explicit skip with reason), tests cover the new behavior, and Step 6 quotes status, Content-Type, and the relevant headers or body from the environment you actually hit.

Reference files

FileRead when
references/docs-afdocs.mdMintlify Agent Score, AFDocs, llms.txt, .md URLs, Accept negotiation, page size, auth gates
references/api-surfaces.mdIs Agentic API findings: JSON errors, OpenAPI, versioning, rate limits, function calling, CLI, MCP
references/site-discovery.mdIs It Agent Ready: robots, sitemap, Link headers, DNS-AID, well-known catalogs, bot rules
references/verification.mdStep 6: curl recipes and what counts as evidence

Priority

  1. Failures the product actually has (docs HTML that agents cannot read, HTML error pages on a real API, gated public docs with no alternate path).
  2. Warnings on those same surfaces (llms.txt coverage, buried directives, wrong Content-Type).
  3. Recommended checks that match a surface already in the repo (OpenAPI, MCP, OAuth).
  4. Emerging extras (commerce protocols, A2A, DNS-AID) only when the product already offers them or the user asked to add them.

A missing MCP card is not a failure on a site that has no MCP server. Do not invent an API, CLI, or payment protocol to chase points.

Output

Group work by check. For each: evidence from the report, files changed, exact markup or schema added, verification quote, skip reason if N/A.

Finish with remaining items that need a product decision, DNS access, or credentials.

Gotchas

  • llms.txt that lists HTML while .md twins exist steers agents away from markdown and is scored worse than linking .md from the start.
  • A directive in <head>, nav, or past 50% of the HTML body does not count. Put it in the document body, near the top, server-rendered.
  • Returning markdown with Content-Type: text/plain or text/html is a warn, not a pass. Set text/markdown; charset=utf-8 and Vary: Accept.
  • Mentioning a CLI or MCP server in llms.txt without a published package or live endpoint is a partial that you cannot fix with copy. Ship it or stop advertising it.
  • Empty /.well-known/ documents and stub OpenAPI files fail typed-schema checks. Advertise only what exists.
  • Cross-host redirects for llms.txt or docs URLs fail agents that do not follow them. Same-host 3xx, or serve the file on the docs host.
  • Coverage fail vs curated index: regenerate from the sitemap when the site intends parity; if the index is intentional, say so and do not pad it with marketing URLs.

Related skills

  • docs-writing: page quality after the machine-readable path works
  • seo: App Router llms.txt and markdown routes, sitemaps, robots, crawler policy, and AEO measurement. This skill owns the AFDocs/Is Agentic contract those routes must satisfy.
  • dx-audit: the CLI or SDK once it exists as a package
  • ax-audit: in-product agent trust, not public HTTP discovery
  • agents-md: repo instruction files, not public llms.txt

Maintenance only: evals/evals.json is for changing this skill, not for a user task.

Sources

Drew process and check lists from AFDocs / Agent-Friendly Documentation Spec, Mintlify Agent Score, Is Agentic, and Is It Agent Ready. Left their scoring UIs and the official Is Agentic retrieve-a-report skill. Authored the inspect-then-implement loop, skip rules for absent surfaces, and verification contract.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Agent Ready AI skill do?

Implements agent-readiness on public sites and docs from Mintlify Agent Score, AFDocs, Is Agentic, and Is It Agent Ready reports. Covers llms.txt, markdown negotiation, OpenAPI errors, rate-limit headers, MCP discovery, and robots Link headers. Use when asked to "make this agent-ready", "improve Agent Score", "fix llms.txt coverage", or when a pasted isitagentready, is-agentic, or mintlify/score report is the brief. For docs prose use docs-writing; for CLI/SDK ergonomics use dx-audit; for agentic product UX use ax-audit; for crawler policy and Next.js markdown routes use seo.

Why use Agent Ready on TypingMind?

Because you install it once and use it with any model. Agent Ready is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Agent Ready in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mblode/agent-skills/tree/main/skills/agent-ready. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Agent Ready?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Agent Ready?

As many as you like. As long as a model supports skills, you can use Agent Ready with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Agent Ready AI skill free?

Yes. It is published on GitHub by mblode under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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