Csv Data Summarizer

Name: Csv Data Summarizer
Author: ovachiever

ovachiever/droid-tings

72 installs
51 repo stars
Updated November 25, 2025
ovachiever/droid-tings

This is a copy of csv-data-summarizer by coffeefuelbump - installs and ranking accrue to the original listing.

Helps with ai & agent building tasks during AI-assisted development.

About

csv-data-summarizer is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted coding.

csv-data-summarizer
AI & Agent Building
AI-coding skill

Csv Data Summarizer by the numbers

72 all-time installs (skills.sh)
Data as of Jul 27, 2026 (Skillselion catalog sync)

npx skills add https://github.com/ovachiever/droid-tings --skill csv-data-summarizer

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Installs	72
repo stars	★ 51
Last updated	November 25, 2025
Repository	ovachiever/droid-tings ↗

What it does

Helps with ai & agent building tasks during AI-assisted development.

Files

SKILL.mdMarkdownGitHub ↗

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

# Python
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
env/
venv/
.venv/

# Distribution / packaging
*.egg-info/
dist/
build/

# Jupyter Notebook
.ipynb_checkpoints

# IDE
.vscode/
.idea/
*.swp
*.swo
*~

# Project specific
*.png
*.jpg
*.jpeg
chart.png
correlation_heatmap.png
time_series_analysis.png
distributions.png
categorical_distributions.png

# Allow the skill zip file to be committed
!csv-data-summarizer.zip

# OS
.DS_Store
Thumbs.db

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from pathlib import Path

def summarize_csv(file_path):
    """
    Comprehensively analyzes a CSV file and generates multiple visualizations.
    
    Args:
        file_path (str): Path to the CSV file
        
    Returns:
        str: Formatted comprehensive analysis of the dataset
    """
    df = pd.read_csv(file_path)
    summary = []
    charts_created = []
    
    # Basic info
    summary.append("=" * 60)
    summary.append("📊 DATA OVERVIEW")
    summary.append("=" * 60)
    summary.append(f"Rows: {df.shape[0]:,} | Columns: {df.shape[1]}")
    summary.append(f"\nColumns: {', '.join(df.columns.tolist())}")
    
    # Data types
    summary.append(f"\n📋 DATA TYPES:")
    for col, dtype in df.dtypes.items():
        summary.append(f"  • {col}: {dtype}")
    
    # Missing data analysis
    missing = df.isnull().sum().sum()
    missing_pct = (missing / (df.shape[0] * df.shape[1])) * 100
    summary.append(f"\n🔍 DATA QUALITY:")
    if missing:
        summary.append(f"Missing values: {missing:,} ({missing_pct:.2f}% of total data)")
        summary.append("Missing by column:")
        for col in df.columns:
            col_missing = df[col].isnull().sum()
            if col_missing > 0:
                col_pct = (col_missing / len(df)) * 100
                summary.append(f"  • {col}: {col_missing:,} ({col_pct:.1f}%)")
    else:
        summary.append("✓ No missing values - dataset is complete!")
    
    # Numeric analysis
    numeric_cols = df.select_dtypes(include='number').columns.tolist()
    if numeric_cols:
        summary.append(f"\n📈 NUMERICAL ANALYSIS:")
        summary.append(str(df[numeric_cols].describe()))
        
        # Correlations if multiple numeric columns
        if len(numeric_cols) > 1:
            summary.append(f"\n🔗 CORRELATIONS:")
            corr_matrix = df[numeric_cols].corr()
            summary.append(str(corr_matrix))
            
            # Create correlation heatmap
            plt.figure(figsize=(10, 8))
            sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', center=0, 
                       square=True, linewidths=1)
            plt.title('Correlation Heatmap')
            plt.tight_layout()
            plt.savefig('correlation_heatmap.png', dpi=150)
            plt.close()
            charts_created.append('correlation_heatmap.png')
    
    # Categorical analysis
    categorical_cols = df.select_dtypes(include=['object']).columns.tolist()
    categorical_cols = [c for c in categorical_cols if 'id' not in c.lower()]
    
    if categorical_cols:
        summary.append(f"\n📊 CATEGORICAL ANALYSIS:")
        for col in categorical_cols[:5]:  # Limit to first 5
            value_counts = df[col].value_counts()
            summary.append(f"\n{col}:")
            for val, count in value_counts.head(10).items():
                pct = (count / len(df)) * 100
                summary.append(f"  • {val}: {count:,} ({pct:.1f}%)")
    
    # Time series analysis
    date_cols = [c for c in df.columns if 'date' in c.lower() or 'time' in c.lower()]
    if date_cols:
        summary.append(f"\n📅 TIME SERIES ANALYSIS:")
        date_col = date_cols[0]
        df[date_col] = pd.to_datetime(df[date_col], errors='coerce')
        
        date_range = df[date_col].max() - df[date_col].min()
        summary.append(f"Date range: {df[date_col].min()} to {df[date_col].max()}")
        summary.append(f"Span: {date_range.days} days")
        
        # Create time-series plots for numeric columns
        if numeric_cols:
            fig, axes = plt.subplots(min(3, len(numeric_cols)), 1, 
                                    figsize=(12, 4 * min(3, len(numeric_cols))))
            if len(numeric_cols) == 1:
                axes = [axes]
            
            for idx, num_col in enumerate(numeric_cols[:3]):
                ax = axes[idx] if len(numeric_cols) > 1 else axes[0]
                daily_data = df.groupby(date_col)[num_col].agg(['mean', 'sum', 'count'])
                daily_data['mean'].plot(ax=ax, label='Average', linewidth=2)
                ax.set_title(f'{num_col} Over Time')
                ax.set_xlabel('Date')
                ax.set_ylabel(num_col)
                ax.legend()
                ax.grid(True, alpha=0.3)
            
            plt.tight_layout()
            plt.savefig('time_series_analysis.png', dpi=150)
            plt.close()
            charts_created.append('time_series_analysis.png')
    
    # Distribution plots for numeric columns
    if numeric_cols:
        n_cols = min(4, len(numeric_cols))
        fig, axes = plt.subplots(2, 2, figsize=(12, 10))
        axes = axes.flatten()
        
        for idx, col in enumerate(numeric_cols[:4]):
            axes[idx].hist(df[col].dropna(), bins=30, edgecolor='black', alpha=0.7)
            axes[idx].set_title(f'Distribution of {col}')
            axes[idx].set_xlabel(col)
            axes[idx].set_ylabel('Frequency')
            axes[idx].grid(True, alpha=0.3)
        
        # Hide unused subplots
        for idx in range(len(numeric_cols[:4]), 4):
            axes[idx].set_visible(False)
        
        plt.tight_layout()
        plt.savefig('distributions.png', dpi=150)
        plt.close()
        charts_created.append('distributions.png')
    
    # Categorical distributions
    if categorical_cols:
        fig, axes = plt.subplots(2, 2, figsize=(14, 10))
        axes = axes.flatten()
        
        for idx, col in enumerate(categorical_cols[:4]):
            value_counts = df[col].value_counts().head(10)
            axes[idx].barh(range(len(value_counts)), value_counts.values)
            axes[idx].set_yticks(range(len(value_counts)))
            axes[idx].set_yticklabels(value_counts.index)
            axes[idx].set_title(f'Top Values in {col}')
            axes[idx].set_xlabel('Count')
            axes[idx].grid(True, alpha=0.3, axis='x')
        
        # Hide unused subplots
        for idx in range(len(categorical_cols[:4]), 4):
            axes[idx].set_visible(False)
        
        plt.tight_layout()
        plt.savefig('categorical_distributions.png', dpi=150)
        plt.close()
        charts_created.append('categorical_distributions.png')
    
    # Summary of visualizations
    if charts_created:
        summary.append(f"\n📊 VISUALIZATIONS CREATED:")
        for chart in charts_created:
            summary.append(f"  ✓ {chart}")
    
    summary.append("\n" + "=" * 60)
    summary.append("✅ COMPREHENSIVE ANALYSIS COMPLETE")
    summary.append("=" * 60)
    
    return "\n".join(summary)


if __name__ == "__main__":
    # Test with sample data
    import sys
    if len(sys.argv) > 1:
        file_path = sys.argv[1]
    else:
        file_path = "resources/sample.csv"
    
    print(summarize_csv(file_path))

month,year,quarter,product_line,total_revenue,cost_of_goods_sold,gross_profit,gross_margin_pct,marketing_expense,sales_expense,rd_expense,admin_expense,total_operating_expenses,operating_income,operating_margin_pct,interest_expense,tax_expense,net_income,net_margin_pct,customer_acquisition_cost,customer_lifetime_value,units_sold,avg_selling_price,headcount,revenue_per_employee
Jan,2023,Q1,SaaS Platform,450000,135000,315000,70.0,65000,85000,45000,35000,230000,85000,18.9,5000,16000,64000,14.2,125,2400,1200,375,45,10000
Jan,2023,Q1,Enterprise Solutions,280000,112000,168000,60.0,35000,55000,25000,20000,135000,33000,11.8,3000,6600,23400,8.4,450,8500,450,622,45,6222
Jan,2023,Q1,Professional Services,125000,50000,75000,60.0,15000,22000,8000,12000,57000,18000,14.4,1500,3600,12900,10.3,200,3200,95,1316,45,2778
Feb,2023,Q1,SaaS Platform,475000,142500,332500,70.0,68000,89000,47000,36000,240000,92500,19.5,5200,18500,68800,14.5,120,2500,1300,365,47,10106
Feb,2023,Q1,Enterprise Solutions,295000,118000,177000,60.0,38000,58000,27000,22000,145000,32000,10.8,3200,6400,22400,7.6,440,8600,470,628,47,6277
Feb,2023,Q1,Professional Services,135000,54000,81000,60.0,16000,24000,9000,13000,62000,19000,14.1,1600,3800,13600,10.1,195,3300,105,1286,47,2872
Mar,2023,Q1,SaaS Platform,520000,156000,364000,70.0,75000,95000,52000,40000,262000,102000,19.6,5500,19250,77250,14.9,115,2650,1450,359,50,10400
Mar,2023,Q1,Enterprise Solutions,325000,130000,195000,60.0,42000,63000,30000,25000,160000,35000,10.8,3500,7000,24500,7.5,425,8800,520,625,50,6500
Mar,2023,Q1,Professional Services,148000,59200,88800,60.0,18000,26000,10000,14000,68000,20800,14.1,1800,4160,14840,10.0,190,3400,115,1287,50,2960
Apr,2023,Q2,SaaS Platform,555000,166500,388500,70.0,80000,100000,55000,42000,277000,111500,20.1,5800,22300,83400,15.0,110,2750,1550,358,52,10673
Apr,2023,Q2,Enterprise Solutions,340000,136000,204000,60.0,45000,65000,32000,26000,168000,36000,10.6,3700,7200,25100,7.4,420,9000,540,630,52,6538
Apr,2023,Q2,Professional Services,158000,63200,94800,60.0,19000,27000,11000,15000,72000,22800,14.4,1900,4560,16340,10.3,185,3500,125,1264,52,3038
May,2023,Q2,SaaS Platform,590000,177000,413000,70.0,85000,105000,58000,44000,292000,121000,20.5,6000,24200,90800,15.4,105,2850,1650,358,55,10727
May,2023,Q2,Enterprise Solutions,365000,146000,219000,60.0,48000,68000,35000,28000,179000,40000,11.0,4000,8000,28000,7.7,410,9200,580,629,55,6636
May,2023,Q2,Professional Services,172000,68800,103200,60.0,21000,29000,12000,16000,78000,25200,14.7,2100,5040,18060,10.5,180,3600,135,1274,55,3127
Jun,2023,Q2,SaaS Platform,625000,187500,437500,70.0,90000,110000,62000,46000,308000,129500,20.7,6200,25850,97450,15.6,100,2950,1750,357,58,10776
Jun,2023,Q2,Enterprise Solutions,385000,154000,231000,60.0,50000,70000,37000,29000,186000,45000,11.7,4200,9000,31800,8.3,400,9400,610,631,58,6638
Jun,2023,Q2,Professional Services,185000,74000,111000,60.0,22000,31000,13000,17000,83000,28000,15.1,2200,5580,20220,10.9,175,3700,145,1276,58,3190
Jul,2023,Q3,SaaS Platform,665000,199500,465500,70.0,95000,115000,65000,48000,323000,142500,21.4,6500,28500,107500,16.2,95,3050,1850,359,60,11083
Jul,2023,Q3,Enterprise Solutions,410000,164000,246000,60.0,53000,73000,40000,31000,197000,49000,12.0,4400,9800,34800,8.5,390,9600,650,631,60,6833
Jul,2023,Q3,Professional Services,198000,79200,118800,60.0,24000,33000,14000,18000,89000,29800,15.1,2400,5960,21440,10.8,170,3800,155,1277,60,3300
Aug,2023,Q3,SaaS Platform,705000,211500,493500,70.0,100000,120000,68000,50000,338000,155500,22.1,6800,31100,117600,16.7,90,3150,1950,362,63,11190
Aug,2023,Q3,Enterprise Solutions,435000,174000,261000,60.0,56000,76000,42000,33000,207000,54000,12.4,4600,10800,38600,8.9,380,9800,690,630,63,6905
Aug,2023,Q3,Professional Services,210000,84000,126000,60.0,25000,35000,15000,19000,94000,32000,15.2,2500,6400,23100,11.0,165,3900,165,1273,63,3333
Sep,2023,Q3,SaaS Platform,750000,225000,525000,70.0,108000,128000,72000,53000,361000,164000,21.9,7200,33360,123440,16.5,88,3250,2080,360,65,11538
Sep,2023,Q3,Enterprise Solutions,465000,186000,279000,60.0,60000,80000,45000,35000,220000,59000,12.7,5000,11800,42200,9.1,370,10000,735,633,65,7154
Sep,2023,Q3,Professional Services,225000,90000,135000,60.0,27000,37000,16000,20000,100000,35000,15.6,2700,6920,25380,11.3,160,4000,175,1286,65,3462
Oct,2023,Q4,SaaS Platform,795000,238500,556500,70.0,115000,135000,75000,55000,380000,176500,22.2,7500,35870,133130,16.7,85,3350,2200,361,68,11691
Oct,2023,Q4,Enterprise Solutions,490000,196000,294000,60.0,63000,83000,47000,36000,229000,65000,13.3,5200,13000,46800,9.6,360,10200,770,636,68,7206
Oct,2023,Q4,Professional Services,238000,95200,142800,60.0,29000,39000,17000,21000,106000,36800,15.5,2800,7360,26640,11.2,158,4100,185,1286,68,3500
Nov,2023,Q4,SaaS Platform,840000,252000,588000,70.0,122000,142000,78000,58000,400000,188000,22.4,7800,38440,141760,16.9,82,3450,2320,362,70,12000
Nov,2023,Q4,Enterprise Solutions,520000,208000,312000,60.0,67000,87000,50000,38000,242000,70000,13.5,5500,14100,50400,9.7,355,10400,815,638,70,7429
Nov,2023,Q4,Professional Services,252000,100800,151200,60.0,31000,41000,18000,22000,112000,39200,15.6,3000,7728,28472,11.3,155,4200,195,1292,70,3600
Dec,2023,Q4,SaaS Platform,895000,268500,626500,70.0,130000,150000,82000,62000,424000,202500,22.6,8200,41145,153155,17.1,80,3550,2480,361,72,12431
Dec,2023,Q4,Enterprise Solutions,555000,222000,333000,60.0,72000,92000,53000,40000,257000,76000,13.7,6000,15400,54600,9.8,350,10600,870,638,72,7708
Dec,2023,Q4,Professional Services,268000,107200,160800,60.0,33000,43000,19000,23000,118000,42800,16.0,3200,8352,31248,11.7,152,4300,205,1307,72,3722
Jan,2024,Q1,SaaS Platform,925000,277500,647500,70.0,135000,155000,85000,64000,439000,208500,22.5,8500,42070,157930,17.1,78,3650,2550,363,75,12333
Jan,2024,Q1,Enterprise Solutions,575000,230000,345000,60.0,75000,95000,55000,42000,267000,78000,13.6,6200,15760,56040,9.7,345,10800,900,639,75,7667
Jan,2024,Q1,Professional Services,280000,112000,168000,60.0,34000,45000,20000,24000,123000,45000,16.1,3300,8770,32930,11.8,150,4400,215,1302,75,3733
Feb,2024,Q1,SaaS Platform,965000,289500,675500,70.0,140000,160000,88000,66000,454000,221500,23.0,8800,44510,168190,17.4,75,3750,2660,363,77,12532
Feb,2024,Q1,Enterprise Solutions,600000,240000,360000,60.0,78000,98000,57000,43000,276000,84000,14.0,6400,16800,60800,10.1,340,11000,940,638,77,7792
Feb,2024,Q1,Professional Services,295000,118000,177000,60.0,36000,47000,21000,25000,129000,48000,16.3,3500,9420,35080,11.9,148,4500,225,1311,77,3831
Mar,2024,Q1,SaaS Platform,1020000,306000,714000,70.0,148000,168000,92000,69000,477000,237000,23.2,9200,47880,179920,17.6,73,3850,2810,363,80,12750
Mar,2024,Q1,Enterprise Solutions,635000,254000,381000,60.0,82000,103000,60000,45000,290000,91000,14.3,6800,18200,66000,10.4,335,11200,990,641,80,7938
Mar,2024,Q1,Professional Services,312000,124800,187200,60.0,38000,49000,22000,26000,135000,52200,16.7,3700,10230,38270,12.3,145,4600,240,1300,80,3900

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</div>

---

📊 CSV Data Summarizer - Claude Skill

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!

![Version](https://github.com/coffeefuelbump/csv-data-summarizer-claude-skill) ![Python](https://www.python.org/) ![License](LICENSE)

</div>

🚀 Features

🤖 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

📥 Quick Download

Get Started in 2 Steps

1️⃣ Download the Skill ![Download Skill](https://github.com/coffeefuelbump/csv-data-summarizer-claude-skill/raw/main/csv-data-summarizer.zip)

2️⃣ Try the Demo Data ![Download Demo CSV](https://github.com/coffeefuelbump/csv-data-summarizer-claude-skill/raw/main/examples/showcase_financial_pl_data.csv)

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📦 What's Included

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

🎯 How It Works

1. Upload any CSV file to Claude.ai 2. Skill activates automatically when CSV is detected 3. Analysis runs immediately - inspects data structure and adapts 4. Results delivered - Complete analysis with multiple visualizations

No prompting needed. No options to choose. Just instant, comprehensive insights!

📥 Installation

For Claude.ai Users

1. Download the latest release: `csv-data-summarizer.zip` 2. Go to Claude.ai → Settings → Capabilities → Skills 3. Upload the zip file 4. Enable the skill 5. Done! Upload any CSV and watch it work ✨

For Developers

git clone git@github.com:coffeefuelbump/csv-data-summarizer-claude-skill.git
cd csv-data-summarizer-claude-skill
pip install -r requirements.txt

📊 Sample Dataset Highlights

The 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

🎨 Example Use Cases

📊 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

🛠️ Technical Details

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

📝 Example Output

============================================================
📊 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

🌟 Connect & Learn More

![Join AI Community-blue?style=for-the-badge&logo=data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIyNCIgaGVpZ2h0PSIyNCIgdmlld0JveD0iMCAwIDI0IDI0IiBmaWxsPSJ3aGl0ZSI+PHBhdGggZD0iTTEyIDJDNi40OCAyIDIgNi40OCAyIDEyczQuNDggMTAgMTAgMTAgMTAtNC40OCAxMC0xMFMxNy41MiAyIDEyIDJ6bTAgM2MxLjY2IDAgMyAxLjM0IDMgM3MtMS4zNCAzLTMgMy0zLTEuMzQtMy0zIDEuMzQtMyAzLTN6bTAgMTQuMmMtMi41IDAtNC43MS0xLjI4LTYtMy4yMi4wMy0xLjk5IDQtMy4wOCA2LTMuMDggMS45OSAwIDUuOTcgMS4wOSA2IDMuMDgtMS4yOSAxLjk0LTMuNSAzLjIyLTYgMy4yMnoiLz48L3N2Zz4=)](https://www.skool.com/ai-for-your-business/about)

![Link Tree](https://linktr.ee/corbin_brown)

![YouTube Membership](https://www.youtube.com/channel/UCJFMlSxcvlZg5yZUYJT0Pug/join)

![Twitter Follow](https://twitter.com/corbin_braun)

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🤝 Contributing

Contributions are welcome! Feel free to:

Report bugs
Suggest new features
Submit pull requests
Share your use cases

📄 License

MIT License - feel free to use this skill for personal or commercial projects!

🙏 Acknowledgments

Built for the Claude Skills platform by Anthropic.

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Made with ❤️ for the AI community

⭐ Star this repo if you find it useful!

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CSV Data Summarizer - Resources

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🌟 Connect & Learn More

🚀 Join Our Community

🔗 All My Links

![Link Tree](https://linktr.ee/corbin_brown)

🛠️ Become a Builder

![YouTube Membership](https://www.youtube.com/channel/UCJFMlSxcvlZg5yZUYJT0Pug/join)

🐦 Follow on Twitter

![Twitter Follow](https://twitter.com/corbin_braun)

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Sample Data

The sample.csv file contains example sales data with the following columns:

date: Transaction date
product: Product name (Widget A, B, or C)
quantity: Number of items sold
revenue: Total revenue from the transaction
customer_id: Unique customer identifier
region: Geographic region (North, South, East, West)

Usage Examples

Basic Summary

Analyze sample.csv

With Custom CSV

Here's my sales_data.csv file. Can you summarize it?

Focus on Specific Insights

What are the revenue trends in this dataset?

Testing the Skill

You can test the skill locally before uploading to Claude:

# Install dependencies
pip install -r ../requirements.txt

# Run the analysis
python ../analyze.py sample.csv

Expected Output

The analysis will provide:

1. Dataset dimensions - Row and column counts 2. Column information - Names and data types 3. Summary statistics - Mean, median, std dev, min/max for numeric columns 4. Data quality - Missing value detection and counts 5. Visualizations - Time-series plots when date columns are present

Customization

To adapt this skill for your specific use case:

1. Modify analyze.py to include domain-specific calculations 2. Add custom visualization types in the plotting section 3. Include validation rules specific to your data 4. Add more sample datasets to test different scenarios

date,product,quantity,revenue,customer_id,region
2024-01-15,Widget A,5,129.99,C001,North
2024-01-16,Widget B,3,89.97,C002,South
2024-01-17,Widget A,7,181.98,C003,East
2024-01-18,Widget C,2,199.98,C001,North
2024-01-19,Widget B,4,119.96,C004,West
2024-01-20,Widget A,6,155.94,C005,South
2024-01-21,Widget C,1,99.99,C002,South
2024-01-22,Widget B,8,239.92,C006,East
2024-01-23,Widget A,3,77.97,C007,North
2024-01-24,Widget C,5,499.95,C003,East
2024-01-25,Widget B,2,59.98,C008,West
2024-01-26,Widget A,9,233.91,C004,West
2024-01-27,Widget C,3,299.97,C009,North
2024-01-28,Widget B,6,179.94,C010,South
2024-01-29,Widget A,4,103.96,C005,South
2024-01-30,Widget C,7,699.93,C011,East
2024-01-31,Widget B,5,149.95,C012,West
2024-02-01,Widget A,8,207.92,C013,North
2024-02-02,Widget C,2,199.98,C014,South
2024-02-03,Widget B,10,299.90,C015,East