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Combine quantitative insights with qualitative research recommendations.\n\n## How It Works\n\n### Step 1: Read and Validate Your Data\n- Accept CSV, Excel, or JSON data files with user cohort information\n- Verify data structure: cohort identifier, time periods, engagement metrics\n- Check for missing values and data quality issues\n- Summarize key statistics (cohort sizes, date ranges, metrics available)\n\n### Step 2: Generate Quantitative Analysis\n- Calculate cohort retention rates and engagement trends\n- Identify retention curves, drop-off patterns, and anomalies\n- Compute feature adoption rates across cohorts\n- Calculate month-over-month or period-over-period changes\n- Generate Python analysis scripts using pandas and numpy if requested\n\n### Step 3: Create Visualizations\n- Generate retention heatmaps (cohorts vs. time periods)\n- Create line charts showing cohort progression\n- Build comparison charts for feature adoption\n- Visualize drop-off points and engagement trends\n- Output as interactive charts or static images\n\n### Step 4: Identify Insights & Patterns\n- Spot one or more significant patterns:\n  - Early churn in specific cohorts\n  - Late-stage engagement changes\n  - Feature adoption clusters\n  - Seasonal or temporal trends\n- Highlight surprising findings and deviations\n- Compare cohort performance to establish baselines\n\n### Step 5: Suggest Follow-Up Research\n- Recommend qualitative research methods:\n  - Targeted user interviews with churning users\n  - Feature usage surveys with engaged cohorts\n  - Session replays of key interaction patterns\n  - Win\u002Floss analysis for high vs. low retention cohorts\n- Design follow-up quantitative studies\n- Suggest A\u002FB tests or feature experiments\n\n## Usage Examples\n\n**Example 1: Upload CSV Data**\n```\nUpload cohort_engagement.csv with columns: cohort_month, weeks_active,\nuser_id, feature_x_usage, engagement_score\n\nRequest: \"Analyze retention patterns and identify why Q4 2025 cohorts\nunderperform compared to Q3\"\n```\n\n**Example 2: Describe Data Format**\n```\n\"I have monthly user cohorts from Jan-Dec 2025. Each row shows:\ncohort date, user ID, purchase frequency, and support tickets.\nAnalyze which cohorts show best long-term retention.\"\n```\n\n**Example 3: Feature Adoption Analysis**\n```\nUpload feature_usage.xlsx with cohort adoption data.\n\nRequest: \"Compare adoption curves for our new feature across cohorts.\nWhich cohorts adopted fastest? Any patterns?\"\n```\n\n## Key Capabilities\n\n- **Data Reading**: Import CSV, Excel, JSON, SQL query results\n- **Retention Analysis**: Calculate and visualize retention rates over time\n- **Cohort Comparison**: Compare metrics across cohort groups\n- **Anomaly Detection**: Flag unusual patterns or drop-offs\n- **Python Scripts**: Generate reusable analysis code for ongoing analysis\n- **Visualizations**: Create heatmaps, charts, and interactive dashboards\n- **Research Design**: Suggest targeted follow-up studies and interview approaches\n- **Statistical Summary**: Provide quantitative metrics and correlation analysis\n\n## Tips for Best Results\n\n1. **Include time dimension**: Provide data across multiple time periods\n2. **Define cohort clearly**: Make cohort grouping explicit (signup month, feature launch date, etc.)\n3. **Provide context**: Explain product changes, launches, or events during the period\n4. **Multiple metrics**: Include retention, engagement, feature usage, revenue, etc.\n5. **Sufficient data**: At least 3-4 cohorts for meaningful pattern identification\n6. **Request specific output**: Ask for visualizations, Python scripts, or research recommendations\n\n## Output Format\n\nYou'll receive:\n- **Data Summary**: Cohort overview and data quality assessment\n- **Quantitative Findings**: Key metrics, retention rates, and trend analysis\n- **Visualizations**: Charts showing retention curves, adoption patterns\n- **Pattern Identification**: 2-3 significant insights from the data\n- **Research Recommendations**: Specific qualitative and quantitative follow-ups\n- **Analysis Scripts** (if requested): Python code for reproducible analysis\n- **Next Steps**: Prioritized actions based on findings\n\n---\n\n### Further Reading\n\n- [Cohort Analysis 101: How to Reduce Churn and Make Better Product Decisions](https:\u002F\u002Fwww.productcompass.pm\u002Fp\u002Fcohort-analysis)\n- [The Product Analytics Playbook: AARRR, HEART, Cohorts & Funnels for PMs](https:\u002F\u002Fwww.productcompass.pm\u002Fp\u002Fthe-product-analytics-playbook-aarrr)\n- [Are You Tracking the Right Metrics?](https:\u002F\u002Fwww.productcompass.pm\u002Fp\u002Fare-you-tracking-the-right-metrics)\n",{},"main","def8531e65114c0fca8fb8551c1871ee0eed705c","skills\u002Fphuryn\u002Fpm-skills\u002Fcohort-analysis",24,"https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub","https:\u002F\u002Fskillshub.wtf","https:\u002F\u002Fgithub.com\u002FComeOnOliver","AI & Blockchain Engineer\r\n","2026-06-24 22:07:19",{"source":7,"skillName":5,"commands":35,"prompt":45,"zipAvailable":46},[36,39,42],{"tool":37,"command":38},"npx","npx skills add ComeOnOliver\u002Fskillshub --skill cohort-analysis",{"tool":40,"command":41},"bunx","bunx skills add ComeOnOliver\u002Fskillshub --skill cohort-analysis",{"tool":43,"command":44},"pnpm","pnpm dlx skills add ComeOnOliver\u002Fskillshub --skill 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