cohort-analysis

Analyze cohort retention patterns and generate retention curves from CSV, Excel, or JSON data.

Updated Apr 8, 2026

Search Tags:#analytics #python #visualization #retention #data-processing #cohort-analysis

Ready to run

npx skills add https://github.com/martiraste-lgtm/claude-skills --skill cohort-analysis-martiraste-lgtm

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: cohort-analysis
Source: https://github.com/martiraste-lgtm/claude-skills/tree/main/pm-data-analytics-cohort-analysis
Command: npx skills add https://github.com/martiraste-lgtm/claude-skills --skill cohort-analysis-martiraste-lgtm

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps product teams analyze user engagement by cohort to identify retention patterns, feature adoption differences, and long-term engagement signals for data-driven decisions.

Core Features & Use Cases

Cohort-based retention analysis across time to reveal drop-off points and trend shifts.
Feature adoption and engagement comparison across cohorts with visualizations.
Actionable insights and recommendations for follow-up qualitative and quantitative research.

Quick Start

Upload a cohort data file (CSV, Excel, or JSON) and request retention and adoption analysis.

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 trends and cohort drop-off points?▼

Analyzing user retention trends requires uploading cohort data files containing cohort identifiers, time periods, and engagement metrics. The analysis identifies drop-off points and trend shifts across time, generating retention curves and actionable insights for product teams.

What is cohort analysis and how does it reveal feature adoption patterns?▼

Cohort analysis is a technique that quantifies retention patterns and feature adoption differences across user groups over time. By comparing engagement metrics across cohorts, it reveals long-term engagement signals and generates visualizations for data-driven product decisions.

Can I visualize retention curves and adoption metrics directly from a CSV file?▼

Yes, you can visualize retention curves and adoption metrics from a CSV file. The analysis reads CSV, Excel, or JSON formats containing cohort identifiers and engagement data, automatically generating adoption visuals and retention curves for cohort comparison.

How do I generate reproducible Python scripts for cohort retention analysis?▼

Generating reproducible Python scripts for cohort retention analysis involves processing your data through the analysis workflow. The system delivers optional Python scripts alongside retention curves and insights, ensuring your cohort comparison and anomaly detection results can be replicated.

What data structure do I need to perform cohort retention analysis?▼

To perform cohort retention analysis, you need data structured with cohort identifiers, time periods, and engagement metrics. Supported file formats include CSV, Excel, and JSON, which are read to identify retention patterns, detect anomalies, and generate trend insights.

Does cohort analysis work for detecting anomalies in long-term engagement signals?▼

Yes, cohort analysis works for detecting anomalies in long-term engagement signals. The analysis applies anomaly detection to cohort data, identifying unusual retention patterns and trend shifts across time periods to reveal significant deviations from expected engagement behavior.