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skills/csv-data-summarizer/SKILL.md

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+---
+name: csv-data-summarizer
+description: Analyzes CSV files, generates summary stats, and plots quick visualizations using Python and pandas.
+metadata:
+  version: 2.1.0
+  dependencies: python>=3.8, pandas>=2.0.0, matplotlib>=3.7.0, seaborn>=0.12.0
+---
+
+# CSV Data Summarizer
+
+This Skill analyzes CSV files and provides comprehensive summaries with statistical insights and visualizations.
+
+## When to Use This Skill
+
+Claude should use this Skill whenever the user:
+- Uploads or references a CSV file
+- Asks to summarize, analyze, or visualize tabular data
+- Requests insights from CSV data
+- Wants to understand data structure and quality
+
+## How It Works
+
+## ⚠️ CRITICAL BEHAVIOR REQUIREMENT ⚠️
+
+**DO NOT ASK THE USER WHAT THEY WANT TO DO WITH THE DATA.**
+**DO NOT OFFER OPTIONS OR CHOICES.**
+**DO NOT SAY "What would you like me to help you with?"**
+**DO NOT LIST POSSIBLE ANALYSES.**
+
+**IMMEDIATELY AND AUTOMATICALLY:**
+1. Run the comprehensive analysis
+2. Generate ALL relevant visualizations
+3. Present complete results
+4. NO questions, NO options, NO waiting for user input
+
+**THE USER WANTS A FULL ANALYSIS RIGHT AWAY - JUST DO IT.**
+
+### Automatic Analysis Steps:
+
+**The skill intelligently adapts to different data types and industries by inspecting the data first, then determining what analyses are most relevant.**
+
+1. **Load and inspect** the CSV file into pandas DataFrame
+2. **Identify data structure** - column types, date columns, numeric columns, categories
+3. **Determine relevant analyses** based on what's actually in the data:
+   - **Sales/E-commerce data** (order dates, revenue, products): Time-series trends, revenue analysis, product performance
+   - **Customer data** (demographics, segments, regions): Distribution analysis, segmentation, geographic patterns
+   - **Financial data** (transactions, amounts, dates): Trend analysis, statistical summaries, correlations
+   - **Operational data** (timestamps, metrics, status): Time-series, performance metrics, distributions
+   - **Survey data** (categorical responses, ratings): Frequency analysis, cross-tabulations, distributions
+   - **Generic tabular data**: Adapts based on column types found
+
+4. **Only create visualizations that make sense** for the specific dataset:
+   - Time-series plots ONLY if date/timestamp columns exist
+   - Correlation heatmaps ONLY if multiple numeric columns exist
+   - Category distributions ONLY if categorical columns exist
+   - Histograms for numeric distributions when relevant
+   
+5. **Generate comprehensive output** automatically including:
+   - Data overview (rows, columns, types)
+   - Key statistics and metrics relevant to the data type
+   - Missing data analysis
+   - Multiple relevant visualizations (only those that apply)
+   - Actionable insights based on patterns found in THIS specific dataset
+   
+6. **Present everything** in one complete analysis - no follow-up questions
+
+**Example adaptations:**
+- Healthcare data with patient IDs → Focus on demographics, treatment patterns, temporal trends
+- Inventory data with stock levels → Focus on quantity distributions, reorder patterns, SKU analysis  
+- Web analytics with timestamps → Focus on traffic patterns, conversion metrics, time-of-day analysis
+- Survey responses → Focus on response distributions, demographic breakdowns, sentiment patterns
+
+### Behavior Guidelines
+
+✅ **CORRECT APPROACH - SAY THIS:**
+- "I'll analyze this data comprehensively right now."
+- "Here's the complete analysis with visualizations:"
+- "I've identified this as [type] data and generated relevant insights:"
+- Then IMMEDIATELY show the full analysis
+
+✅ **DO:**
+- Immediately run the analysis script
+- Generate ALL relevant charts automatically
+- Provide complete insights without being asked
+- Be thorough and complete in first response
+- Act decisively without asking permission
+
+❌ **NEVER SAY THESE PHRASES:**
+- "What would you like to do with this data?"
+- "What would you like me to help you with?"
+- "Here are some common options:"
+- "Let me know what you'd like help with"
+- "I can create a comprehensive analysis if you'd like!"
+- Any sentence ending with "?" asking for user direction
+- Any list of options or choices
+- Any conditional "I can do X if you want"
+
+❌ **FORBIDDEN BEHAVIORS:**
+- Asking what the user wants
+- Listing options for the user to choose from
+- Waiting for user direction before analyzing
+- Providing partial analysis that requires follow-up
+- Describing what you COULD do instead of DOING it
+
+### Usage
+
+The Skill provides a Python function `summarize_csv(file_path)` that:
+- Accepts a path to a CSV file
+- Returns a comprehensive text summary with statistics
+- Generates multiple visualizations automatically based on data structure
+
+### Example Prompts
+
+> "Here's `sales_data.csv`. Can you summarize this file?"
+
+> "Analyze this customer data CSV and show me trends."
+
+> "What insights can you find in `orders.csv`?"
+
+### Example Output
+
+**Dataset Overview**
+- 5,000 rows × 8 columns  
+- 3 numeric columns, 1 date column  
+
+**Summary Statistics**
+- Average order value: $58.2  
+- Standard deviation: $12.4
+- Missing values: 2% (100 cells)
+
+**Insights**
+- Sales show upward trend over time
+- Peak activity in Q4
+*(Attached: trend plot)*
+
+## Files
+
+- `analyze.py` - Core analysis logic
+- `requirements.txt` - Python dependencies
+- `resources/sample.csv` - Example dataset for testing
+- `resources/README.md` - Additional documentation
+
+## Notes
+
+- Automatically detects date columns (columns containing 'date' in name)
+- Handles missing data gracefully
+- Generates visualizations only when date columns are present
+- All numeric columns are included in statistical summary
+