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-specgit 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-spec)<a href="https://agentmods.dev/skills/mohitagw15856/pm-claude-skills/agent-spec"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/agent-spec/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-spec"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/agent-spec.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 | $0.00088 | $0.01033 |
| Opus 5.5 | $0.00035 | $0.00413 |
| Sonnet 5.5 | $0.00018 | $0.00207 |
| Haiku 4.5 | $0.00009 | $0.00103 |
Grade A, and why
agent-spec 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Spec Skill
An agent is a model plus tools plus a loop — and the danger lives in the tools and the loop, not the model. This skill specifies an agent so its authority is explicit: what it can do, what needs a human yes, and what happens when it's wrong. Scope and guardrails first; cleverness second.
Not quite this? Use
agent-observability-specwhen the agent is in production and needs tracing and alerting.
Required Inputs
Ask for these only if they aren't already provided:
- Job to be done — the outcome the agent owns, and the boundary of its authority.
- Tools/actions — what it can call (read APIs, write actions, code execution), and which are irreversible.
- Autonomy level — fully autonomous, propose-then-approve, or co-pilot.
- Risk surface — what's the worst thing a wrong action could do (spend money, send a message, delete data)?
- Success definition & escalation — how "done" is judged, and when it must hand off to a human.
Output Format
Agent Spec: [name]
1. Goal & scope — the job in one sentence; explicit non-goals and authority limits.
2. Tools / actions — a table; mark each action's reversibility and required permission.
| Tool | Purpose | Reversible? | Gate |
|---|---|---|---|
| search_kb | read context | yes | none |
| send_email | notify | no | human approval |
3. Control loop — plan → act → observe → reflect; the stopping condition; and a hard max-steps / max-cost budget so it can't loop forever.
4. Guardrails & approval gates — which actions require a human yes (default: anything irreversible, outbound, or spending), input/output validation, and allow/deny lists. Pair irreversible actions with a dry-run preview (see action-runner).
5. Memory & state — what it remembers within a task vs. across tasks, and where (link a professional-brain for durable memory).
6. Escalation & handoff — the triggers that stop the agent and route to a human (low confidence, repeated failure, out-of-scope request, high-risk action).
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 · 76 lines · 88 tokens per session scan A 00b22352a07d
agent-spec is a skill published in the GitHub repository mohitagw15856/pm-claude-skills (1,449 stars, last pushed today), licensed MIT. It adds 88 tokens to every session and 1,033 once invoked, about $0.0004 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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