cohort-analysis

name	cohort-analysis
description	Use when a task needs retention analysis, cohort behavior comparison, activation-metric discovery, or diagnosis of how user groups perform over time.
compatibility	opencode
metadata	{"model":"gpt-5.4","model_reasoning_effort":"high","sandbox_mode":"read-only"}

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.