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Cohort Analysis

Pawel HurynCommunityCreated by Pawel Huryn

  • Skill
  • Research and analysis
  • Data and AI
  • MIT
  • No issues found
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From the author

Perform cohort analysis on user engagement data — retention curves, feature adoption trends, and segment-level insights. Use when analyzing user retention by cohort, studying feature adoption over time, investigating churn patterns, or identifying engagement trends.
— the description by Pawel Huryn

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The Cohort Analysis & Retention Explorer is a specialized tool designed to help users understand user behavior over time. By processing engagement data, it identifies retention curves, feature adoption trends, and churn patterns. The skill combines quantitative data processing with actionable qualitative research recommendations, providing a comprehensive view of how different user groups interact with a product.

What it does

  • Imports and validates user engagement data from CSV, Excel, or JSON formats.
  • Calculates retention rates and engagement metrics across defined user cohorts.
  • Generates visual representations including retention heatmaps and progression charts.
  • Identifies significant behavioral patterns, anomalies, and drop-off points.
  • Provides Python analysis scripts for reproducible data exploration.
  • Suggests targeted qualitative research methods to investigate findings.

When to use it

  • When you need to compare long-term retention across different user signup months.
  • When investigating why specific cohorts exhibit higher churn rates than others.
  • When analyzing the speed and depth of feature adoption across user segments.
  • When you need to visualize engagement trends to support product decision-making.
  • When preparing to conduct follow-up user interviews or A/B testing.

Advantages

  • Automates the complex calculation of cohort-based retention metrics.
  • Provides both high-level visual summaries and granular statistical insights.
  • Bridges the gap between raw data analysis and actionable research strategy.
  • Supports reproducible workflows through generated Python scripts.
  • Helps identify specific product events or time periods that impact user behavior.

Usage examples

“Analyze retention patterns and identify why Q4 2025 cohorts underperform compared to Q3 using the uploaded CSV.”
The agent provides a retention heatmap, identifies the specific drop-off week for Q4 cohorts, and suggests conducting exit surveys for those users.
“Compare adoption curves for our new feature across cohorts. Which cohorts adopted fastest?”
The agent generates a comparative line chart of feature usage and identifies which cohort reached peak adoption most rapidly.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/phuryn/cohort-analysis/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip cohort-analysis.zip -d .agents/skills

Documentation by Pawel Huryn

Cohort Analysis & Retention Explorer

Purpose

Analyze user engagement and retention patterns by cohort to identify trends in user behavior, feature adoption, and long-term engagement. Combine quantitative insights with qualitative research recommendations.

How It Works

Step 1: Read and Validate Your Data

  • Accept CSV, Excel, or JSON data files with user cohort information

  • Verify data structure: cohort identifier, time periods, engagement metrics

  • Check for missing values and data quality issues

  • Summarize key statistics (cohort sizes, date ranges, metrics available)

Step 2: Generate Quantitative Analysis

  • Calculate cohort retention rates and engagement trends

  • Identify retention curves, drop-off patterns, and anomalies

  • Compute feature adoption rates across cohorts

  • Calculate month-over-month or period-over-period changes

  • Generate Python analysis scripts using pandas and numpy if requested

Step 3: Create Visualizations

  • Generate retention heatmaps (cohorts vs. time periods)

  • Create line charts showing cohort progression

  • Build comparison charts for feature adoption

  • Visualize drop-off points and engagement trends

  • Output as interactive charts or static images

Step 4: Identify Insights & Patterns

  • Spot one or more significant patterns:

    • Early churn in specific cohorts

    • Late-stage engagement changes

    • Feature adoption clusters

    • Seasonal or temporal trends

  • Highlight surprising findings and deviations

  • Compare cohort performance to establish baselines

Step 5: Suggest Follow-Up Research

  • Recommend qualitative research methods:

    • Targeted user interviews with churning users

    • Feature usage surveys with engaged cohorts

    • Session replays of key interaction patterns

    • Win/loss analysis for high vs. low retention cohorts

  • Design follow-up quantitative studies

  • Suggest A/B tests or feature experiments

Usage Examples

Example 1: Upload CSV Data

Upload cohort_engagement.csv with columns: cohort_month, weeks_active,
user_id, feature_x_usage, engagement_score

Request: "Analyze retention patterns and identify why Q4 2025 cohorts
underperform compared to Q3"

Example 2: Describe Data Format

"I have monthly user cohorts from Jan-Dec 2025. Each row shows:
cohort date, user ID, purchase frequency, and support tickets.
Analyze which cohorts show best long-term retention."

Example 3: Feature Adoption Analysis

Upload feature_usage.xlsx with cohort adoption data.

Request: "Compare adoption curves for our new feature across cohorts.
Which cohorts adopted fastest? Any patterns?"

Key Capabilities

  • Data Reading: Import CSV, Excel, JSON, SQL query results

  • Retention Analysis: Calculate and visualize retention rates over time

  • Cohort Comparison: Compare metrics across cohort groups

  • Anomaly Detection: Flag unusual patterns or drop-offs

  • Python Scripts: Generate reusable analysis code for ongoing analysis

  • Visualizations: Create heatmaps, charts, and interactive dashboards

  • Research Design: Suggest targeted follow-up studies and interview approaches

  • Statistical Summary: Provide quantitative metrics and correlation analysis

Tips for Best Results

  1. Include time dimension: Provide data across multiple time periods

  2. Define cohort clearly: Make cohort grouping explicit (signup month, feature launch date, etc.)

  3. Provide context: Explain product changes, launches, or events during the period

  4. Multiple metrics: Include retention, engagement, feature usage, revenue, etc.

  5. Sufficient data: At least 3-4 cohorts for meaningful pattern identification

  6. Request specific output: Ask for visualizations, Python scripts, or research recommendations

Output Format

You'll receive:

  • Data Summary: Cohort overview and data quality assessment

  • Quantitative Findings: Key metrics, retention rates, and trend analysis

  • Visualizations: Charts showing retention curves, adoption patterns

  • Pattern Identification: 2-3 significant insights from the data

  • Research Recommendations: Specific qualitative and quantitative follow-ups

  • Analysis Scripts (if requested): Python code for reproducible analysis

  • Next Steps: Prioritized actions based on findings

Further Reading

Files

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
Pawel Huryn
Publisher
Pawel Huryn (Community)
Source
phuryn/pm-skills · pm-data-analytics/skills/cohort-analysis · commit 2a12295
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Pawel Huryn or Pawel Huryn.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download cohort-analysis.zip