Cohort analysis

Skill dills122/ai-central/templates/skills/imported/pm-skills/pm-data-analytics/skills/cohort-analysis

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.From its SKILL.md

Install

npx -y skills add dills122/ai-central --skill cohort-analysis

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SKILL.md

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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
Define cohort clearly: Make cohort grouping explicit (signup month, feature launch date, etc.)
Provide context: Explain product changes, launches, or events during the period
Multiple metrics: Include retention, engagement, feature usage, revenue, etc.
Sufficient data: At least 3-4 cohorts for meaningful pattern identification
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

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most analytics metrics skills give in 980 tokens

Counted across 333 of the 342 authors here whose files we hold, read 2026-09-06

Read product marketing context before asking questionsin 37 of 333, across 16 files
Test one variable at a timein 26 of 333, across 11 files
Pre-determine sample size before launchin 24 of 333, across 16 files
Verify tracking and QA variants before launchin 17 of 333, across 8 files
Monitor for technical issues during the testin 14 of 333, across 6 files
Match each save offer to the cancel reasonin 14 of 333, across 5 files
Start every test with a specific hypothesisin 14 of 333, across 7 files
Keep the continue-cancelling option visiblein 13 of 333, across 4 files
Document every test with hypothesis, variants, results, and learningsin 13 of 333, across 6 files
Gather churn, billing, product, usage, and constraint context firstin 12 of 333, across 3 files
Build a health score from weighted signalsin 12 of 333, across 3 files
Retry soft declines 3-5 times over 7-10 daysin 12 of 333, across 3 files

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.