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 autopilot-chartergit 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/autopilot-charter)<a href="https://agentmods.dev/skills/mohitagw15856/pm-claude-skills/autopilot-charter"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/autopilot-charter/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/autopilot-charter"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/autopilot-charter.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 | $0.00086 | $0.01093 |
| Opus 5.5 | $0.00034 | $0.00437 |
| Sonnet 5.5 | $0.00017 | $0.00219 |
| Haiku 4.5 | $0.00009 | $0.00109 |
Grade A, and why
autopilot-charter 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.
Autopilot Charter Skill
Inventory the reports, briefings, and reviews you produce on a rhythm, and decide — deliberately — which ones an AI should run on a schedule, which it should only draft, and which stay human.
What This Skill Produces
- A ritual inventory: every recurring artifact, its cadence, audience, and inputs
- An automate / assist / keep-manual call per ritual, with the reason
- Guardrails for each automated ritual (review gate, failure behaviour, escalation)
- A rollout order — which ritual to automate first and why
Required Inputs
Ask for (if not already provided):
- The recurring outputs the user or team produces (weekly updates, monthly reviews, monitors, digests)
- Who consumes each one and what they do with it
- Where the inputs live (git, analytics, CRM, inbox, notes) and whether an agent can reach them
- Tolerance for error per artifact — what happens if a run is wrong or missing?
Classification Framework
Score each ritual on four questions, then classify:
| Question | Points toward automating |
|---|---|
| Inputs reachable? Can an agent read the sources without a human fetching them? | Yes |
| Structure stable? Does the output look the same every cycle? | Yes |
| Cost of a bad run? Would a wrong or stale edition mislead a decision? | Low cost |
| Delta-shaped? Is the value "what changed since last time" rather than fresh judgement? | Yes |
- Automate — all four favourable. Schedule it end-to-end; the human sees the result, not the work.
- Assist — structure is stable but judgement or unreachable inputs remain. Schedule a draft; a human finishes it.
- Keep manual — high cost of error, or the ritual's value is the human thinking (performance feedback, strategy). Do not automate; record why so nobody re-litigates it.
Guardrails (required for every "Automate")
For each automated ritual, define:
- Review gate — does an edition ship unreviewed, or land as a draft for approval? Default to draft for anything audience-facing.
- Failure behaviour — if a run fails or a source is unreachable, does it skip, retry, or alert? A silent gap is worse than an error message.
- Staleness marker — every edition states when it ran and which sources it read.
- Kill criteria — what result (two wrong editions? a complaint from the audience?) takes it off autopilot.
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 · 86 tokens per session scan A 309b1eccfabf
autopilot-charter is a skill published in the GitHub repository mohitagw15856/pm-claude-skills (1,449 stars, last pushed today), licensed MIT. It adds 86 tokens to every session and 1,093 once invoked, about $0.0003 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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