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Agent Readiness Audit

CommunityPopular
mohitagw15856
agent-readiness-audit

Audit whether AI agents can actually use your product — docs, APIs, onboarding, errors, and discoverability, evaluated from a non-human user's perspective. Use when asked if a product is agent-ready, to audit a site or API for AI usability, to prepare for agentic traffic, or when agents keep failing against your product. Produces a scored readiness report with per-surface findings and a prioritised fix list. For optimising a single article for AI citation use aeo-optimizer; for designing the MCP server itself use mcp-server-spec.

Overview

Publishermohitagw15856
Repositorypm-claude-skills
Skill nameagent-readiness-audit
Stars
1.4K
Forks
240
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by mohitagw15856 on GitHub. Read the source before you install it.

Installation

Install the Agent Readiness Audit 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/mohitagw15856/pm-claude-skills.git /tmp/pm-claude-skills
mkdir -p .claude/skills
cp -r /tmp/pm-claude-skills/exports/openclaw/agent-readiness-audit .claude/skills/agent-readiness-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Readiness Audit 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 Readiness Audit 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 Readiness Audit 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 Readiness Audit Skill

A growing share of your product's users aren't human: agents research it, evaluate it, onboard onto it, and operate it on their principals' behalf. They can't watch your demo video, guess what an unlabeled icon means, or call support. This skill audits every surface an agent touches and scores how much of your product is invisible or unusable to them.

What This Skill Produces

  • A readiness score by surface (discovery, docs, API/auth, errors, onboarding, transactions)
  • Per-surface findings with the failing artifact quoted and the fix
  • A prioritised fix list ranked by agent-traffic impact vs effort
  • A re-test protocol so readiness is measured, not vibed

Required Inputs

Ask for (if not already provided):

  • The product and its public surfaces (site, docs URL, API reference, status page)
  • What agents will be asked to do with it — research/compare? sign up? operate it daily?
  • What exists already: llms.txt? MCP server? OpenAPI spec? If unknown, the audit checks
  • Any observed agent failures (the best audit seed there is)

The Audit Surfaces

Walk each surface asking one question: could a capable agent, starting cold, complete its job here without a human unblocking it?

1. Discovery — can agents find and understand what you are? llms.txt present and current · docs fetchable as clean markdown/text (not JS-rendered walls) · pricing and limits stated in prose an agent can quote · comparison-relevant facts (SOC 2, SSO, data residency) written down anywhere at all — an agent can't infer what you never wrote.

2. Docs — written for readers who execute? Every task documented as copy-runnable steps with expected outputs · code samples that actually run (agents execute them verbatim) · one canonical way per task (agents can't arbitrate between three contradictory tutorials) · error-message strings from the product appearing verbatim in the docs so search-by-error works.

3. API & auth — self-serve without a human? Key/token obtainable without a sales call (or the agent path is documented honestly) · OpenAPI spec accurate to the deployed API · rate limits discoverable programmatically · an MCP server, or at least a stated position on one.

4. Errors — instructive to a retrying machine? Errors name the field and the fix · machine-readable codes stable across releases · 4xx vs 5xx used honestly (agents branch on this) · no CAPTCHAs on API-adjacent flows without a documented alternative.

5. Onboarding & transactions — can an agent complete them? Signup/checkout completable without image CAPTCHAs, drag-widgets, or SMS-only verification (or agent-appropriate alternatives exist) · forms with real labels, not placeholder-only · the confirmation state readable as text.

6. Guardrails — do you know your agent traffic? Are agents distinguishable in analytics? Is there a stated policy (terms + technical) for agent use — welcome, gated, or forbidden? Silence is a decision made by accident.

Score each surface 0-4: 0 = actively hostile · 2 = humans-only assumptions throughout · 4 = agent-native. Cite the failing artifact for anything below 3.

Output Format

Agent Readiness Audit: [product] — [n]/24

SurfaceScore /4Sharpest finding

Findings (per surface, worst first) [surface] — [score]: [what fails, with the artifact quoted] → Fix: [specific change]

Fix list, prioritised:

#FixSurfaceImpactEffort

Re-test protocol: [5-8 cold-start agent tasks ("sign up and send one API request", "find whether SSO is on the cheap plan") — run them with a real agent after fixes; the score is the pass rate, not the checklist]

Quality Checks

  • Every score below 3 cites the actual failing artifact (URL, error string, form field), not a vibe
  • Fixes are specific changes, not "improve the docs"
  • The audit distinguishes unwritten facts (agent can't know) from buried facts (agent might find)
  • The fix list is ranked by agent-traffic impact, and states assumptions where traffic is unmeasured
  • The re-test protocol exists — readiness is a pass rate, not an opinion

Anti-Patterns

  • Do not audit from memory of the product — fetch the actual surfaces; they've changed
  • Do not treat "we have great docs" as evidence — great-for-humans routinely scores 1/4 for agents
  • Do not recommend blocking agents as a fix unless the business genuinely wants that — then say it in terms and technically, consistently
  • Do not conflate this with SEO/AEO — being quotable is surface 1; being usable is the other five
  • Do not skip the guardrails surface — unmeasured agent traffic is how products discover this problem in an outage

Frequently asked questions

What does the Agent Readiness Audit AI skill do?

Audit whether AI agents can actually use your product — docs, APIs, onboarding, errors, and discoverability, evaluated from a non-human user's perspective. Use when asked if a product is agent-ready, to audit a site or API for AI usability, to prepare for agentic traffic, or when agents keep failing against your product. Produces a scored readiness report with per-surface findings and a prioritised fix list. For optimising a single article for AI citation use aeo-optimizer; for designing the MCP server itself use mcp-server-spec.

Why use Agent Readiness Audit on TypingMind?

Because you install it once and use it with any model. Agent Readiness Audit 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 Readiness Audit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mohitagw15856/pm-claude-skills/tree/main/exports/openclaw/agent-readiness-audit. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Agent Readiness Audit?

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 Readiness Audit?

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

Is the Agent Readiness Audit AI skill free?

Yes. It is published on GitHub by mohitagw15856 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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