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cohort-analysis

Analyze user retention, feature adoption, and engagement trends across cohorts.

1|Updated May 6, 2026
Search Tags:#engagement#pandas#retention#cohort analysis#churn#product analytics#feature adoption
One-click install
npx skills add https://github.com/moonzff/Codex_up --skill cohort-analysis-moonzff
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cohort-analysis
Source: https://github.com/moonzff/Codex_up/tree/main/skills/metrics-experiments/cohort-analysis
Command: npx skills add https://github.com/moonzff/Codex_up --skill cohort-analysis-moonzff

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Understanding why some user groups retain while others churn requires tedious manual slicing of engagement data. This Skill automates cohort retention analysis so you can quickly spot drop-off patterns, compare cohort performance, and decide where to investigate further. ## Core Features & Use Cases - Retention Analysis: Calculate cohort retention rates, build retention curves and heatmaps, and flag anomalies or early churn in specific cohorts. - Feature Adoption Tracking: Compare feature adoption curves across cohorts to see which groups adopted fastest and why. - Research Recommendations: Suggest follow-up qualitative studies (user interviews, surveys, session replays) and quantitative experiments based on findings. - Use Case: Upload a CSV of monthly user cohorts with engagement scores and ask why Q4 2025 cohorts underperform Q3 — receive retention curves, pattern insights, and prioritized next steps. ## Quick Start Upload your cohort engagement CSV and ask to analyze retention patterns and identify which cohorts churn earliest.

Frequently Asked Questions about cohort-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I analyze user retention by cohort?▼

Provide data with a cohort identifier (such as signup month), time periods, and engagement metrics. The analysis calculates retention rates per cohort, builds retention curves and heatmaps, and highlights drop-off points and anomalies.

What data format is needed for cohort analysis?▼

CSV, Excel, or JSON files work, with columns for cohort identifier, time period, user ID, and engagement metrics like feature usage or engagement score. At least 3-4 cohorts across multiple time periods are needed for meaningful patterns.

Can cohort analysis compare feature adoption across user groups?▼

Yes. Provide feature usage data per cohort and the analysis computes adoption rates, compares adoption curves across cohorts, and identifies which groups adopted fastest along with possible explanations.

Does cohort analysis generate Python code for repeatable analysis?▼

Yes, on request it produces reusable Python scripts using pandas and numpy so you can rerun the same retention and adoption calculations on updated data without repeating manual steps.

What are the limitations of cohort retention analysis?▼

Results depend on data quality and volume: fewer than 3-4 cohorts or missing time periods weaken conclusions. Quantitative patterns alone cannot explain causation, so follow-up interviews or surveys are recommended to understand why cohorts behave differently.

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