Agent skill

Agent Readiness Audit

by mohitagw15856 in mohitagw15856/pm-claude-skills

Audit whether AI agents can actually use your product — docs, APIs, onboarding, errors, and discoverability, evaluated from a non-human user's perspective.

MITAuto-check passedMarketing & SEO

Install Agent Readiness Audit

skills CLI
$ npx skills add mohitagw15856/pm-claude-skills --skill agent-readiness-audit -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mohitagw15856/pm-claude-skills agent-readiness-audit --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/agent-readiness-audit .claude/skills/agent-readiness-audit && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
agent-readiness-audit
GitHub stars
1.4k
Token cost
~1.4k tokens
SKILL.md length
708 words
Files
1
Skills in repo
1,348
Repo updated
First seen
Licence
MIT

At a glance

Audit whether AI agents can actually use your product — docs, APIs, onboarding, errors, and discoverability, evaluated from a non-human user's perspective.

  • Asked if a product is agent-ready
  • SKILL.md covers What This Skill Produces, Required Inputs, The Audit Surfaces and Output Format, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • API for AI usability

What it does

Agent Readiness Audit is an agent skill from mohitagw15856/pm-claude-skills. 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…

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Marketing & SEO, covering AI search optimization, UX design and MCP servers. It works with Model Context Protocol. The repository describes itself as: 1255 professional Agent Skills for Claude, ChatGPT, Gemini, Cursor & Codex — PRDs, postmortems, leases, medical bills, layoffs, go-bags, new countries. Plain markdown, MIT, in… The licence is MIT.

When your agent uses it

  • Asked if a product is agent-ready
  • API for AI usability
  • Prepare for agentic traffic
  • Agents keep failing against your product

Example prompts

  • “/agent-readiness-audit”

What it can do on your machine

Read from SKILL.md and the folder at commit 1cbf1f0. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Agent Readiness Audit loads about 1.4k tokens when it runs. Until then it costs about 139 tokens; SKILL.md has 708 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~139
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from mohitagw15856/pm-claude-skills at commit 1cbf1f0, republished under its MIT licence (© mohitagw15856). 708 words, ~1,381 tokens.

Download SKILL.mdSave it as .claude/skills/agent-readiness-audit/SKILL.md (or your agent's skills folder).
name
agent-readiness-audit
description
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.

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.

Show full SKILL.md (248 more words)Show less

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

Example Trigger Phrases

  • "If a product is agent-ready."
  • "Audit a site or API for AI usability."
  • "Prepare for agentic traffic."

© mohitagw15856, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/agent-readiness-audit of mohitagw15856/pm-claude-skills.

Open the folder on GitHubat commit 1cbf1f0

Compare with similar skills

Agent Readiness Audit next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Agent Readiness Audit compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Agent Readiness Audit this skillmohitagw15856/pm-claude-skills1.4k—~1.4kAutomated safety check: PassMIT
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Lognormsickn33/agentic-awesome-skills47k—~1.4kAutomated safety check: PassMIT
SEO Dataforseohashgraph-online/awesome-codex-plugins1.3k—~4.5kAutomated safety check: NotesMIT
Conference Developer Endpointsswyxio/skills176—~1.9kAutomated safety check: PassMIT
SEO DataforseoAgriciDaniel/codex-seo7992 repos~4.6kAutomated safety check: PassMIT

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Categories

Questions about Agent Readiness Audit

What does Agent Readiness Audit do?

Audit whether AI agents can actually use your product — docs, APIs, onboarding, errors, and discoverability, evaluated from a non-human user's perspective. Agent Readiness Audit is an agent skill from mohitagw15856/pm-claude-skills. Audit whether AI agents can actually use your product — docs, APIs, onboarding, errors, and discoverability, evaluated from a non-human user's perspective.

When should I use Agent Readiness Audit?

Agent Readiness Audit fits situations like: asked if a product is agent-ready; API for AI usability; prepare for agentic traffic; agents keep failing against your product.

How do I install Agent Readiness Audit in Claude Code?

Run `npx skills add mohitagw15856/pm-claude-skills --skill agent-readiness-audit -a claude-code`. Or copy the skill folder (skills/agent-readiness-audit in mohitagw15856/pm-claude-skills) into .claude/skills/agent-readiness-audit in your project. Claude Code loads it when a task matches its description.

How do I install Agent Readiness Audit in Codex?

Run `npx skills add mohitagw15856/pm-claude-skills --skill agent-readiness-audit -a codex`. Or copy the skill folder (skills/agent-readiness-audit in mohitagw15856/pm-claude-skills) into .agents/skills/agent-readiness-audit in your project. Codex loads it when a task matches its description.

Can I use Agent Readiness Audit in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mohitagw15856/pm-claude-skills --skill agent-readiness-audit -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/agent-readiness-audit, .gemini/skills/agent-readiness-audit, .github/skills/agent-readiness-audit and .opencode/skills/agent-readiness-audit in your project.

What does Agent Readiness Audit need to run?

SKILL.md names no scripts, command-line tools or credentials: Agent Readiness Audit is instructions for the agent only.

Does Agent Readiness Audit access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Agent Readiness Audit safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Agent Readiness Audit use?

Agent Readiness Audit is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Agent Readiness Audit use?

About 1.4k tokens (SKILL.md is roughly 5.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Agent Readiness Audit?

Skills that share tags, products or a category with Agent Readiness Audit: Canonry (Canonry/canonry, 171 stars), Lognorm (sickn33/agentic-awesome-skills, 47k stars), SEO Dataforseo (hashgraph-online/awesome-codex-plugins, 1.3k stars) and Conference Developer Endpoints (swyxio/skills, 176 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Agent Readiness Audit?

mohitagw15856 (a GitHub user) maintains it in mohitagw15856/pm-claude-skills, which has 1,434 GitHub stars. The repository holds 1,348 skills in this directory. The repository was last updated on October 9, 2026.

Source: mohitagw15856/pm-claude-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.