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/launch-an-ai-feature)<a href="https://agentmods.dev/commands/mohitagw15856/pm-claude-skills/launch-an-ai-feature"><img src="https://agentmods.dev/badge/commands/mohitagw15856/pm-claude-skills/launch-an-ai-feature/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/launch-an-ai-feature"><img src="https://agentmods.dev/badge/commands/mohitagw15856/pm-claude-skills/launch-an-ai-feature.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 | $0.00029 | $0.00423 |
| Opus 5 | $0.00015 | $0.00211 |
| Sonnet 5 | $0.00006 | $0.00085 |
| Haiku 4.5 | $0.00003 | $0.00042 |
Grade A, and why
launch-an-ai-feature 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 12d 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
Run the Launch an AI Feature workflow recipe for: $ARGUMENTS
This is a chain of skills. Run each stage in order and carry every stage's output forward as context for the next — that shared context is the whole point. Open with a one-line plan of the 5 stages, then ask once for any essential missing inputs (the user problem, the stakes / cost of a wrong answer, the data available). Don't re-ask between stages.
Run each stage under a clear ## Stage N — <name> heading:
- Spec it — apply the
ai-feature-prdskill to turn the idea into a PRD built for a probabilistic system: the UX of uncertainty, guardrails, fallback behaviour, and an explicit quality bar tied to the stakes. - Design the system — apply the
rag-design-docskill (or note if an agent is the better fit — seeagent-spec) to design retrieval/generation: chunking, retrieval, reranking, grounded answers, and the failure-mode table. - Plan evaluation — apply the
ai-eval-planskill to define datasets, rubrics, baselines, the explicit ship threshold, and the regression gate. - Budget cost & latency — apply the
llm-cost-latency-budgetskill for per-request token math, model tiering, caching, p95 targets, and spend guardrails. - Document it — apply the
model-cardskill to produce a launch-ready card: intended use, sliced evaluation, limitations, and a rollback trigger.
Do not invent metrics, costs, or eval results — note assumptions instead. After the last stage, end with a 5-bullet "What you now have" recap linking each artifact to the stage that produced it.
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.
- 12d ago First seen · 19 lines · 29 tokens per session scan A 333fefed580c
launch-an-ai-feature is a command published in the GitHub repository mohitagw15856/pm-claude-skills (1,357 stars, last pushed today), licensed MIT. It adds 29 tokens to every session and 423 once invoked, about $0.0001 per session on Opus 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
prompt-history
Manage history of created and optimized prompts.
prompt-optimizer
Analyze and rewrite a prompt to maximize clarity, specificity, and output quality.
test-prompt
Test an AI prompt against multiple scenarios to verify consistent, quality output.
prompt
Transform a vague prompt into a precision-crafted one — for AI generation, prompt templates in services or pipelines, or system prompts — or debug why an AI response is poor.
copy-user
Copies a user's Chili Piper workspace and team memberships (and, optionally, product licenses) to another existing user — for onboarding onto an existing territory or replacing a departing rep.
checklist
Generate a custom checklist for the current feature based on user requirements.