A powerful Claude Skill that automatically analyzes CSV files and generates comprehensive insights with visualizations. Upload any CSV and get instant, intelligent analysis without being asked what you want!
- 🤖 Intelligent & Adaptive - Automatically detects data type (sales, customer, financial, survey, etc.) and applies relevant analysis
- 📈 Comprehensive Analysis - Generates statistics, correlations, distributions, and trends
- 🎨 Auto Visualizations - Creates multiple charts based on what's in your data:
- Time-series plots for date-based data
- Correlation heatmaps for numeric relationships
- Distribution histograms
- Categorical breakdowns
- ⚡ Proactive - No questions asked! Just upload CSV and get complete analysis immediately
- 🔍 Data Quality Checks - Automatically detects and reports missing values
- 📊 Multi-Industry Support - Adapts to e-commerce, healthcare, finance, operations, surveys, and more
csv-data-summarizer-claude-skill/
├── SKILL.md # Claude Skill definition
├── analyze.py # Comprehensive analysis engine
├── requirements.txt # Python dependencies
├── examples/
│ └── showcase_financial_pl_data.csv # Demo P&L financial dataset (15 months, 25 metrics)
└── resources/
├── sample.csv # Example dataset
└── README.md # Usage documentation
- Upload any CSV file to Claude.ai
- Skill activates automatically when CSV is detected
- Analysis runs immediately - inspects data structure and adapts
- Results delivered - Complete analysis with multiple visualizations
No prompting needed. No options to choose. Just instant, comprehensive insights!
- Download the latest release:
csv-data-summarizer.zip - Go to Claude.ai → Settings → Capabilities → Skills
- Upload the zip file
- Enable the skill
- Done! Upload any CSV and watch it work ✨
git clone git@github.com:coffeefuelbump/csv-data-summarizer-claude-skill.git
cd csv-data-summarizer-claude-skill
pip install -r requirements.txtThe included demo CSV contains 15 months of P&L data with:
- 3 product lines (SaaS, Enterprise, Services)
- 25 financial metrics including revenue, expenses, margins, CAC, LTV
- Quarterly trends showing business growth
- Perfect for showcasing time-series analysis, correlations, and financial insights
- 📊 Sales Data → Revenue trends, product performance, regional analysis
- 👥 Customer Data → Demographics, segmentation, geographic patterns
- 💰 Financial Data → Transaction analysis, trend detection, correlations
- ⚙️ Operational Data → Performance metrics, time-series analysis
- 📋 Survey Data → Response distributions, cross-tabulations
Dependencies:
- Python 3.8+
- pandas 2.0+
- matplotlib 3.7+
- seaborn 0.12+
Visualizations Generated:
- Time-series trend plots
- Correlation heatmaps
- Distribution histograms
- Categorical bar charts
============================================================
📊 DATA OVERVIEW
============================================================
Rows: 100 | Columns: 15
📋 DATA TYPES:
• order_date: object
• total_revenue: float64
• customer_segment: object
...
🔍 DATA QUALITY:
✓ No missing values - dataset is complete!
📈 NUMERICAL ANALYSIS:
[Summary statistics for all numeric columns]
🔗 CORRELATIONS:
[Correlation matrix showing relationships]
📅 TIME SERIES ANALYSIS:
Date range: 2024-01-05 to 2024-04-11
Span: 97 days
📊 VISUALIZATIONS CREATED:
✓ correlation_heatmap.png
✓ time_series_analysis.png
✓ distributions.png
✓ categorical_distributions.png
Contributions are welcome! Feel free to:
- Report bugs
- Suggest new features
- Submit pull requests
- Share your use cases
MIT License - feel free to use this skill for personal or commercial projects!
Built for the Claude Skills platform by Anthropic.
Made with ❤️ for the AI community
⭐ Star this repo if you find it useful!