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.
Installs just this skill. Get the whole plugin for auto-invocation.
⚡ How it fires
How this skill gets triggered: by you, by Claude, or both.
Fires itselfClaude auto-loads it when your prompt matches the work.
You can call itInvoke it directly when you want it.
Slash command/cohort-analysis
👁️ Context preview
The summary Claude sees to decide when to auto-load this skill.
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)
