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.
npx skills add mohitagw15856/pm-claude-skills --skill agent-readiness-audit --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 col
- What This Skill Produces
- Required Inputs
- The Audit Surfaces
- Output Format
- Agent Readiness Audit: [product] — [n]/24
- Quality Checks
- Anti-Patterns
What does the agent-readiness-audit 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.
How do I install it?
Run `npx skills add mohitagw15856/pm-claude-skills --skill agent-readiness-audit --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From mohitagw15856/pm-claude-skills, a repository with 1,255 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.