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 tim
Install
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add phuryn/pm-skills --skill cohort-analysis -a cursor -y
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.
Questions
How do I install cohort-analysis?
Download pm-data-analytics/skills/cohort-analysis/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does cohort-analysis live?
The publisher keeps it at https://github.com/phuryn/pm-skills.
Which agents can use cohort-analysis?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.
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.
name: cohort-analysis
description: "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."
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
Include time dimension: Provide data across multiple time periods
Download pm-data-analytics/skills/cohort-analysis/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does cohort-analysis live?
The publisher keeps it at https://github.com/phuryn/pm-skills.
Which agents can use cohort-analysis?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.