CSV Data Summarizer

When to Use This Skill

• 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 ⚠️

1. Run the comprehensive analysis
2. Generate ALL relevant visualizations
3. Present complete results
4. NO questions, NO options, NO waiting for user input

Automatic Analysis Steps:

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

• 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

• "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

• 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

• "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"

• 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

summarize_csv(file_path)

• 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

• 5,000 rows × 8 columns
• 3 numeric columns, 1 date column

• Average order value: $58.2
• Standard deviation: $12.4
• Missing values: 2% (100 cells)

• 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