phuryn/pm-skills is a marketplace of reusable skills, commands, and plugins that guide AI assistants through product-management work such as discovery, strategy, planning, metrics, launches, and growth. It is for product managers and teams using Claude Code, Cowork, or compatible assistants. The catalogue entries are the project's own workflows and extensions.
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/phuryn/pm-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/phuryn/pm-skills/analyze-cohorts)<a href="https://agentmods.dev/commands/phuryn/pm-skills/analyze-cohorts"><img src="https://agentmods.dev/badge/commands/phuryn/pm-skills/analyze-cohorts/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/phuryn/pm-skills/analyze-cohorts"><img src="https://agentmods.dev/badge/commands/phuryn/pm-skills/analyze-cohorts.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 | $0.00016 | $0.00782 |
| Opus 5 | $0.00008 | $0.00391 |
| Sonnet 5 | $0.00003 | $0.00156 |
| Haiku 4.5 | $0.00002 | $0.00078 |
Grade A, and why
analyze-cohorts 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 11d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- analyze-cohorts — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/analyze-cohorts -- Cohort Analysis
Analyze user retention and engagement patterns by cohort. Upload your data or describe what you need, and get retention curves, feature adoption trends, and actionable insights.
Invocation
/analyze-cohorts [upload a CSV of user activity data]
/analyze-cohorts Monthly retention for users who signed up in Jan-Jun, grouped by acquisition channel
/analyze-cohorts Help me set up a cohort analysis for our onboarding redesign
Workflow
Step 1: Accept Data or Define Analysis
Two paths:
- With data: User uploads a CSV/spreadsheet with user-level data (user_id, signup_date, activity_date, event_type, etc.)
- Without data: User describes the analysis they need → generate the SQL query and analysis framework
Step 2: Define Cohorts
Ask:
- What defines a cohort? (signup week/month, acquisition channel, plan tier, first feature used)
- What is the retention event? (login, core action, any activity, purchase)
- What time granularity? (daily, weekly, monthly)
- What time range?
Step 3: Analyze
Apply the cohort-analysis skill:
If data is provided:
- Process the data using Python (pandas) to create cohort tables
- Calculate retention rates per cohort per period
- Generate retention curves
- Identify patterns: improving/declining cohorts, seasonal effects, anomalies
- Compare feature adoption across cohorts
If describing an analysis:
- Design the cohort analysis framework
- Generate SQL queries to extract the data
- Create a template spreadsheet for the analysis
- Define the metrics and visualization approach
Step 4: Generate Report
## Cohort Analysis: [Description]
**Date**: [today]
**Cohort definition**: [e.g., signup month]
**Retention event**: [e.g., completed a project]
**Granularity**: [weekly/monthly]
### Retention Table
| Cohort | Size | Week 1 | Week 2 | Week 3 | ... | Week 12 |
|--------|------|--------|--------|--------|-----|---------|
### Key Findings
1. **[Finding]** — [supporting data]
2. ...
### Cohort Comparison
- **Best-performing cohort**: [which, why]
- **Worst-performing cohort**: [which, why]
- **Trend**: [improving/declining/stable over time]
### Retention Benchmarks
| Period | Your Rate | Industry Benchmark | Gap |
|--------|----------|-------------------|-----|
### Recommendations
1. [What to investigate or change based on findings]
2. ...
### Follow-Up Queries
[SQL queries for deeper investigation]
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.
- 11d ago First seen · 100 lines · 16 tokens per session scan A 33ef1cef6d6d
analyze-cohorts is a command published in the GitHub repository phuryn/pm-skills (26,161 stars, last pushed 2mo ago), licensed MIT. It adds 16 tokens to every session and 782 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
experiment-ideas
Generate several concrete, brain-grounded growth ideas — channel, message, rationale, cost-efficiency — ranked by effort vs. impact.
value-prop-statements
Fan an existing positioning statement out into segment- and channel-specific value-prop copy variants, trace-checked against drift.
gtm-motions
Score and select a GTM motion stack against real deal economics, not a taxonomy tour.
buyer-personas
Map the buying committee, then build alternatives-anchored messaging personas.
ideal-customer-profile
Build, enrich, or audit your ICP — trigger events, buyer map, JTBD, disqualifiers.
positioning-messaging
Build or audit positioning statements, messaging, and related output.