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.
npx skills add phuryn/pm-skills --skill cohort-analysisgit 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/skills/phuryn/pm-skills/cohort-analysis)<a href="https://agentmods.dev/skills/phuryn/pm-skills/cohort-analysis"><img src="https://agentmods.dev/badge/skills/phuryn/pm-skills/cohort-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/skills/phuryn/pm-skills/cohort-analysis"><img src="https://agentmods.dev/badge/skills/phuryn/pm-skills/cohort-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00047 | $0.01043 |
| Opus 5 | $0.00023 | $0.00522 |
| Sonnet 5 | $0.00009 | $0.00209 |
| Haiku 4.5 | $0.00005 | $0.00104 |
Grade A, and why
cohort-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 10d 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
2 near-identical copies found in the catalogue:
- cohort-analysis — 100% identical, 0 lines differ
- cohort-analysis — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cohort Analysis & Retention Explorer
Purpose
Analyze user engagement and retention patterns by cohort to identify trends in user behavior, feature adoption, and long-term engagement. Combine quantitative insights with qualitative research recommendations.
How It Works
Step 1: Read and Validate Your Data
- Accept CSV, Excel, or JSON data files with user cohort information
- Verify data structure: cohort identifier, time periods, engagement metrics
- Check for missing values and data quality issues
- Summarize key statistics (cohort sizes, date ranges, metrics available)
Step 2: Generate Quantitative Analysis
- Calculate cohort retention rates and engagement trends
- Identify retention curves, drop-off patterns, and anomalies
- Compute feature adoption rates across cohorts
- Calculate month-over-month or period-over-period changes
- Generate Python analysis scripts using pandas and numpy if requested
Step 3: Create Visualizations
- Generate retention heatmaps (cohorts vs. time periods)
- Create line charts showing cohort progression
- Build comparison charts for feature adoption
- Visualize drop-off points and engagement trends
- Output as interactive charts or static images
Step 4: Identify Insights & Patterns
- Spot one or more significant patterns:
- Early churn in specific cohorts
- Late-stage engagement changes
- Feature adoption clusters
- Seasonal or temporal trends
- Highlight surprising findings and deviations
- Compare cohort performance to establish baselines
Step 5: Suggest Follow-Up Research
- Recommend qualitative research methods:
- Targeted user interviews with churning users
- Feature usage surveys with engaged cohorts
- Session replays of key interaction patterns
- Win/loss analysis for high vs. low retention cohorts
- Design follow-up quantitative studies
- Suggest A/B tests or feature experiments
Usage Examples
Example 1: Upload CSV Data
Upload cohort_engagement.csv with columns: cohort_month, weeks_active,
user_id, feature_x_usage, engagement_score
Request: "Analyze retention patterns and identify why Q4 2025 cohorts
underperform compared to Q3"
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.
- 10d ago First seen · 115 lines · 47 tokens per session scan A bd721a429e58
cohort-analysis is a skill published in the GitHub repository phuryn/pm-skills (26,161 stars, last pushed 2mo ago), licensed MIT. It adds 47 tokens to every session and 1,043 once invoked, about $0.0002 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.
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pre-mortem
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prioritization-frameworks
Selects and applies the right prioritization framework (9 frameworks: Opportunity Score, ICE, RICE, Eisenhower, Impact vs Effort, Risk vs Reward, Kano, Weighted Decision Matrix, MoSCoW) with PMM interpretation layer and GTM launch tier output (T1–T4). Reads brain context (ICP, positioning, revenue levers) and, when…
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go-to-market-strategy
Assigns launch tier (T1–T4) using a four-signal framework and generates a complete GTM brief with positioning angles, channel strategy, success metrics, and competitive context. Reads brain (ICP, positioning, competitive, proof points) and, when available, guardrails from prior launches the user has logged.