/csv-data-summarizer-claude-skill

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