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/unixcrh/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/unixcrh/phuryn-pm-skills/analyze-cohorts)<a href="https://agentmods.dev/commands/unixcrh/phuryn-pm-skills/analyze-cohorts"><img src="https://agentmods.dev/badge/commands/unixcrh/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/unixcrh/phuryn-pm-skills/analyze-cohorts"><img src="https://agentmods.dev/badge/commands/unixcrh/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 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.
This is a copy
100% identical to analyze-cohorts — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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.
- 12d ago First seen · 100 lines · 16 tokens per session scan A 33ef1cef6d6d
analyze-cohorts is a command published in the GitHub repository unixcrh/phuryn-pm-skills (2 stars, last pushed 6mo 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. It is 100% identical to analyze-cohorts, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.