PM Skills is a collection of plain-Markdown instructions that teach AI assistants structured methods for handling professional, personal, and life-admin tasks. People use it with Claude, ChatGPT, Gemini, Cursor, Codex, and other supported agents for work such as writing product requirements, reviewing documents, or planning difficult situations.
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 skills add mohitagw15856/pm-claude-skills --skill agent-readiness-auditgit clone --depth 1 https://github.com/mohitagw15856/pm-claude-skillsWrote 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/mohitagw15856/pm-claude-skills/agent-readiness-audit)<a href="https://agentmods.dev/skills/mohitagw15856/pm-claude-skills/agent-readiness-audit"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/agent-readiness-audit/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mohitagw15856/pm-claude-skills/agent-readiness-audit"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/agent-readiness-audit.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00117 | $0.01290 |
| Opus 5.5 | $0.00047 | $0.00516 |
| Sonnet 5.5 | $0.00023 | $0.00258 |
| Haiku 4.5 | $0.00012 | $0.00129 |
Grade A, and why
agent-readiness-audit 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 2d 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
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.
- 2d ago First seen · 86 lines · 117 tokens per session scan A 0894a64e0e4b
agent-readiness-audit is a skill published in the GitHub repository mohitagw15856/pm-claude-skills (1,449 stars, last pushed today), licensed MIT. It adds 117 tokens to every session and 1,290 once invoked, about $0.0005 per session on Opus 5.5. 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 2026-10-12.
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