CSV Data Summarizer

CSV Data Summarizer

Analyze CSV files and provide comprehensive summaries with statistical insights and visualizations.

Prerequisites

• Python 3.8+
• pandas, matplotlib, seaborn

Instructions

Critical Behavior

DO NOT ask what the user wants to do with the data. IMMEDIATELY run comprehensive analysis and present results.

Automatic Analysis Steps

1. Load and inspect CSV into pandas DataFrame
2. Identify data structure - column types, dates, numerics, categories
3. Determine relevant analyses based on data type:
   • Sales/E-commerce: Time-series, revenue, product performance
   • Customer data: Distributions, segmentation, geographic patterns
   • Financial data: Trends, summaries, correlations
   • Operational data: Time-series, metrics, distributions
   • Survey data: Frequencies, cross-tabulations
   • Generic: Adapts based on column types

Analysis Output

For each dataset, automatically generate:

1. Data Overview

   • Shape, columns, types
   • Missing values summary
   • Memory usage

2. Statistical Summary

   • Descriptive statistics
   • Distribution analysis
   • Correlation matrix

3. Visualizations

   • Distribution plots
   • Time series (if dates present)
   • Category breakdowns
   • Correlation heatmaps

4. Insights

   • Key findings
   • Anomalies detected
   • Recommendations

Guidelines

1. Run full analysis immediately - no questions
2. Adapt analysis to detected data type
3. Generate ALL relevant visualizations
4. Present complete results without waiting for input
5. Include actionable insights

Notes

• Intelligently adapts to different industries
• Inspects data first, then determines relevant analyses
• No user input required - just provide the CSV

Source: coffeefuelbump/csv-data-summarizer-claude-skill