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.
git 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/commands/mohitagw15856/pm-claude-skills/setup-context)<a href="https://agentmods.dev/commands/mohitagw15856/pm-claude-skills/setup-context"><img src="https://agentmods.dev/badge/commands/mohitagw15856/pm-claude-skills/setup-context/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/commands/mohitagw15856/pm-claude-skills/setup-context"><img src="https://agentmods.dev/badge/commands/mohitagw15856/pm-claude-skills/setup-context.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 | $0.00021 | $0.00261 |
| Opus 5.5 | $0.00008 | $0.00104 |
| Sonnet 5.5 | $0.00004 | $0.00052 |
| Haiku 4.5 | $0.00002 | $0.00026 |
Grade A, and why
setup-context 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 1mo 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.
What it actually says
Set up the user's Skill Memory (pm-context.md).
- If
pm-context.mdalready exists at the project root, read it and offer to update it. Otherwise, base the structure ontemplates/pm-context.example.md. - Interview the user briefly to fill it in — ask only for what's missing, a few at a time, using any seed notes here: $ARGUMENTS. Cover: company/product, stage, primary audience, voice/tone & formatting defaults, the metrics that matter, key competitors + differentiator, and any hard constraints.
- Write a concise, concrete
pm-context.md(no filler). Keep each section to a few lines. - Confirm it's saved and explain the payoff: from now on, when running any skill or workflow recipe, read
pm-context.mdfirst and apply it — match the user's product, audience, and voice — so the first draft is tailored, not generic.
Going forward, treat pm-context.md (if present) as standing context for every skill in this library.
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.
- 1mo ago First seen · 14 lines · 21 tokens per session scan A e562ff423827
setup-context is a command published in the GitHub repository mohitagw15856/pm-claude-skills (1,478 stars, last pushed yesterday), licensed MIT. It adds 21 tokens to every session and 261 once invoked, about $0.0001 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 2026-08-30.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
speckit.bug.fix
Apply the remediation from a bug assessment and record what was changed.
speckit.bug.test
Validate that a previously fixed bug is resolved and record the verification report.