---name: csv-data-summarizerdescription: Analyzes CSV files, generates summary stats, and plots quick visualizations using Python and pandas.metadata: version: 2.1.0dependencies: 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