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/rules/mohitagw15856/pm-claude-skills/product-health-analysis)<a href="https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/product-health-analysis"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/product-health-analysis/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/rules/mohitagw15856/pm-claude-skills/product-health-analysis"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/product-health-analysis.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 | $0.00062 | $0.01196 |
| Opus 5 | $0.00031 | $0.00598 |
| Sonnet 5 | $0.00012 | $0.00239 |
| Haiku 4.5 | $0.00006 | $0.00120 |
Grade A, and why
product-health-analysis 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 9d 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Health Analysis Skill
Transform raw metrics data into a clear health narrative — what's working, what's not, and what needs immediate attention.
Required Inputs
Ask the user for these if not provided:
- Metrics data (current values for key metrics — even rough numbers work)
- Targets or benchmarks (OKR targets, historical baselines, or industry benchmarks)
- Period (week / month / quarter being analysed)
- Product area or segment (are we looking at the whole product or a specific feature?)
Metrics Framework
Analyse across four layers:
- Acquisition — new users, source quality, CAC trends
- Activation — time to first value, onboarding completion rates
- Engagement — DAU/MAU, feature adoption, session depth
- Retention — D1/D7/D30 retention, churn rate, resurrection rate
Process
- For each metric, compare: current period vs. previous period, current vs. target
- Flag anything more than 10% off target as requiring investigation
- Look for correlations — does a drop in activation explain a retention dip 2 weeks later?
- Write a plain-English health summary (no jargon) suitable for sharing with non-data stakeholders
- Recommend top 3 areas for immediate investigation with suggested diagnostic steps
- Validate — Confirm every flagged metric has a plausible root cause hypothesis, not just a raw number, and every recommended action has a specific owner or team
Output Structure
Product Health Report — [Period]
Overall Health: 🟢 On Track / 🟡 Watch / 🔴 Action Required
| Metric | Current | Target | vs. Last Period | Status |
|---|---|---|---|---|
| [metric] | [value] | [target] | [+/-%] | [🟢/🟡/🔴] |
Key Observations: [3-5 bullet observations written in plain English]
Areas Requiring Investigation:
- [Metric + hypothesis + suggested diagnostic]
- [Metric + hypothesis + suggested diagnostic]
- [Metric + hypothesis + suggested diagnostic]
Recommended Actions: [Specific next steps with owners and timelines]
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.
- 9d ago First seen · 87 lines · 62 tokens per session scan A 55aef24a9c6a
product-health-analysis is a cursor rule published in the GitHub repository mohitagw15856/pm-claude-skills (1,357 stars, last pushed yesterday), licensed MIT. It adds 62 tokens to every session and 1,196 once invoked, about $0.0003 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 2026-09-03.
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