# Cohort Analysis

> Use when a task needs retention analysis, cohort behavior comparison, activation-metric discovery, or diagnosis of how user groups perform over time.

- Skill: `jshsakura/cohort-analysis` (Agent Skill)
- Install (CLI): `npx skillmds@latest add jshsakura/cohort-analysis`
- Raw SKILL.md: https://api.skillmd.com/api/skills/jshsakura/cohort-analysis/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Product & Planning
- Author: jshsakura (https://skillmd.com/u/jshsakura)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/jshsakura/cohort-analysis

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## Instructions

Own cohort and retention analysis as early diagnosis of product health and activation.

Read retention curves to separate product-market-fit problems from growth problems before recommending action.

Working mode:
1. Choose the cohort type that fits the question: acquisition, behavioral, or segment.
2. Compute retention (N-day or rolling) and assemble a cohort retention table.
3. Diagnose the retention curve shape and locate the fastest drop-off points.
4. Identify candidate activation metrics and rank product recommendations by expected retention impact.

Focus on:
- acquisition cohorts to test whether newer cohorts retain better over time
- behavioral cohorts to find which early actions predict long-term retention
- segment cohorts to identify the ideal customer profile
- N-day vs rolling retention, choosing the metric that matches usage cadence
- curve diagnosis: healthy (flattens) vs declining vs dying (approaches zero)
- activation "aha moment" via behaviors high-retainers do that low-retainers do not
- a curve approaching zero signals a PMF problem that more acquisition will not fix

Quality checks:
- confirm retention is measured from assignment/signup, not from completion (survivorship bias)
- verify cohort comparisons hold sample sizes and time windows comparable
- ensure activation hypotheses are correlation-aware and labeled as hypotheses
- check that drop-off points are tied to specific timing, not vague trends
- distinguish a product-health problem from a growth/acquisition problem

Return:
- cohort retention table (or the structure to build one)
- retention curve diagnosis (healthy / declining / dying)
- key drop-off points with timing
- activation metric hypothesis with supporting behavioral data
- product recommendations ranked by expected retention impact

Do not attribute retention changes to product improvements without comparable cohorts and consistent measurement unless explicitly requested by the parent agent.