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+---
+description: Perform cohort analysis on user data — retention curves, feature adoption, and engagement trends
+argument-hint: "<data file or description of what to analyze>"
+---
+
+# /analyze-cohorts -- Cohort Analysis
+
+Analyze user retention and engagement patterns by cohort. Upload your data or describe what you need, and get retention curves, feature adoption trends, and actionable insights.
+
+## Invocation
+
+```
+/analyze-cohorts [upload a CSV of user activity data]
+/analyze-cohorts Monthly retention for users who signed up in Jan-Jun, grouped by acquisition channel
+/analyze-cohorts Help me set up a cohort analysis for our onboarding redesign
+```
+
+## Workflow
+
+### Step 1: Accept Data or Define Analysis
+
+Two paths:
+- **With data**: User uploads a CSV/spreadsheet with user-level data (user_id, signup_date, activity_date, event_type, etc.)
+- **Without data**: User describes the analysis they need → generate the SQL query and analysis framework
+
+### Step 2: Define Cohorts
+
+Ask:
+- What defines a cohort? (signup week/month, acquisition channel, plan tier, first feature used)
+- What is the retention event? (login, core action, any activity, purchase)
+- What time granularity? (daily, weekly, monthly)
+- What time range?
+
+### Step 3: Analyze
+
+Apply the **cohort-analysis** skill:
+
+**If data is provided:**
+- Process the data using Python (pandas) to create cohort tables
+- Calculate retention rates per cohort per period
+- Generate retention curves
+- Identify patterns: improving/declining cohorts, seasonal effects, anomalies
+- Compare feature adoption across cohorts
+
+**If describing an analysis:**
+- Design the cohort analysis framework
+- Generate SQL queries to extract the data
+- Create a template spreadsheet for the analysis
+- Define the metrics and visualization approach
+
+### Step 4: Generate Report
+
+```
+## Cohort Analysis: [Description]
+
+**Date**: [today]
+**Cohort definition**: [e.g., signup month]
+**Retention event**: [e.g., completed a project]
+**Granularity**: [weekly/monthly]
+
+### Retention Table
+| Cohort | Size | Week 1 | Week 2 | Week 3 | ... | Week 12 |
+|--------|------|--------|--------|--------|-----|---------|
+
+### Key Findings
+1. **[Finding]** — [supporting data]
+2. ...
+
+### Cohort Comparison
+- **Best-performing cohort**: [which, why]
+- **Worst-performing cohort**: [which, why]
+- **Trend**: [improving/declining/stable over time]
+
+### Retention Benchmarks
+| Period | Your Rate | Industry Benchmark | Gap |
+|--------|----------|-------------------|-----|
+
+### Recommendations
+1. [What to investigate or change based on findings]
+2. ...
+
+### Follow-Up Queries
+[SQL queries for deeper investigation]
+```
+
+If data was provided, save analysis as both markdown report and CSV/spreadsheet.
+
+### Step 5: Offer Next Steps
+
+- "Want me to **segment this further** by another dimension?"
+- "Should I **set up metrics alerts** based on these retention thresholds?"
+- "Want me to **design experiments** to improve retention for the weakest cohort?"
+
+## Notes
+
+- Cohort analysis is only as good as the retention event definition — push for a meaningful action, not just "logged in"
+- Early cohorts often look different due to founding user bias — note this when comparing
+- If retention is calculated using a Python script, save the script so the user can re-run with new data
+- Seasonal effects can masquerade as trends — flag if cohort differences might be calendar-driven