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PythonReactSEOPDF
GitHub Skill

csv-data-summarizer

Analyzes CSV files and generates comprehensive summary statistics and visualizations using Python and pandas - automatically and immediately without asking what the user wants.

leegonzales☆ 15 Étoiles du dépôt5 Forks du dépôt

Installation

Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.

Voir la source ↗Guides

Instructions

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

⚠️ 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.

How It Works

The skill intelligently adapts to different data types by inspecting the data first, then determining what analyses are most relevant:

Automatic Analysis Steps:

  1. Load and inspect - Read CSV into pandas DataFrame
  2. Identify structure - Detect column types, dates, numerics, categories
  3. Determine analyses - Adapt based on actual data content
  4. Generate visualizations - Only those that make sense for this dataset
  5. Present complete output - Everything in one comprehensive response

Only creates visualizations that make sense:

  • 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

Behavior Guidelines

✅ CORRECT APPROACH - SAY THIS:

  • "I'll analyze this data comprehensively right now."
  • "Here's the complete analysis with visualizations:"
  • Then IMMEDIATELY show the full analysis

❌ NEVER SAY THESE PHRASES:

  • "What would you like to do with this data?"
  • "Here are some common options:"
  • "I can create a comprehensive analysis if you'd like!"
  • Any sentence ending with "?" asking for user direction

❌ 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 returns comprehensive text summary with statistics and generates multiple visualizations automatically.

Technical Details

Dependencies: python>=3.8, pandas>=2.0.0, matplotlib>=3.7.0, seaborn>=0.12.0

Files:

  • analyze.py - Core analysis logic
  • requirements.txt - Python dependencies
  • examples/ - Sample datasets for testing

Exemples

Consultez les exemples du dépôt source.

Voir la source ↗

Historique source

Date d’indexation
2026-01-20
Mise à jour de l’index
2026-01-31
Branch
main

Les dates concernent ce répertoire. Consultez le dépôt pour l’historique du code.

GitHub ↗

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