Csv Data Summarizer
- 1.9k installs
- 439 repo stars
- Updated October 16, 2025
- coffeefuelbump/csv-data-summarizer-claude-skill
csv-data-summarizer is an agent skill that Analyzes CSV files, generates summary stats, and plots quick visualizations using Python and pandas.
About
This Skill analyzes CSV files and provides comprehensive summaries with statistical insights and visualizations 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 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 The csv data summarizer agent skill provides documented workflows prerequisites triggers and safety guidance from its SKILL md source Agents load it when user requests match the description and follow step by step instructions without inventing capabilities It integrates with standard agent tooling for the tasks inputs outputs
- description: Analyzes CSV files, generates summary stats, and plots quick visualizations using Python and pandas.
- dependencies: python>=3.8, pandas>=2.0.0, matplotlib>=3.7.0, seaborn>=0.12.0
- This Skill analyzes CSV files and provides comprehensive summaries with statistical insights and visualizations.
- Follow csv-data-summarizer SKILL.md steps and documented constraints.
- Follow csv-data-summarizer SKILL.md steps and documented constraints.
Csv Data Summarizer by the numbers
- 1,931 all-time installs (skills.sh)
- +13 installs in the week ending Aug 2, 2026 (Skillselion tracking)
- Ranked #643 of 16,556 AI & Agent Building skills by installs in the Skillselion catalog
- Security screen: MEDIUM risk (skills.sh audit)
- Data as of Aug 2, 2026 (Skillselion catalog sync)
csv-data-summarizer capabilities & compatibility
- Capabilities
- description: analyzes csv files, generates summa · dependencies: python>=3.8, pandas>=2.0.0, matplo · this skill analyzes csv files and provides compr · follow csv data summarizer skill.md steps and do
- Use cases
- orchestration
What csv-data-summarizer says it does
description: Analyzes CSV files, generates summary stats, and plots quick visualizations using Python and pandas.
dependencies: python>=3.8, pandas>=2.0.0, matplotlib>=3.7.0, seaborn>=0.12.0
This Skill analyzes CSV files and provides comprehensive summaries with statistical insights and visualizations.
npx skills add https://github.com/coffeefuelbump/csv-data-summarizer-claude-skill --skill csv-data-summarizerAdd your badge
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| Installs | 1.9k |
|---|---|
| repo stars | ★ 439 |
| Security audit | 2 / 3 scanners passed |
| Last updated | October 16, 2025 |
| Repository | coffeefuelbump/csv-data-summarizer-claude-skill ↗ |
When should an agent use csv-data-summarizer and what problem does it solve?
Analyzes CSV files, generates summary stats, and plots quick visualizations using Python and pandas.
Who is it for?
Developers invoking csv-data-summarizer as documented in the skill source.
Skip if: Skip when requirements fall outside csv-data-summarizer documented scope.
When should I use this skill?
Analyzes CSV files, generates summary stats, and plots quick visualizations using Python and pandas.
What you get
Outputs aligned with the csv-data-summarizer SKILL.md workflow and stated deliverables.
- Statistical summary
- Data-quality report
- Visualization plots
By the numbers
- Skill version 2.1.0
- Requires pandas>=2.0.0, matplotlib>=3.7.0, and seaborn>=0.12.0
Files
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: $12.4
- Missing values: 2% (100 cells)
Insights
- Sales show upward trend over time
- Peak activity in Q4
(Attached: trend plot)
Files
analyze.py- Core analysis logicrequirements.txt- Python dependenciesresources/sample.csv- Example dataset for testingresources/README.md- Additional documentation
Notes
- Automatically detects date columns (columns containing 'date' in name)
- Handles missing data gracefully
- Generates visualizations only when date columns are present
- All numeric columns are included in statistical summary
# Python
__pycache__/
*.py[cod]
*$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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📊 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!
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🚀 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
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Get Started in 2 Steps
1️⃣ Download the Skill 
2️⃣ Try the Demo Data 
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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
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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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pandas>=2.0.0
matplotlib>=3.7.0
seaborn>=0.12.0
CSV Data Summarizer - Resources
---
🌟 Connect & Learn More
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🚀 Join Our Community
🔗 All My Links
🛠️ Become a Builder
🐦 Follow on Twitter
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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.csvWith 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.csvExpected 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
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Csv Data Summarizer has 2 known copies in the catalog totaling 121 installs. They canonicalize to this original listing.
- ovachiever - 72 installs
- jackspace - 49 installs
How it compares
Pick csv-data-summarizer over manual notebook setup when you need instant EDA summaries and plots the moment a CSV appears in chat.
FAQ
What is csv-data-summarizer?
Analyzes CSV files, generates summary stats, and plots quick visualizations using Python and pandas.
When should I use csv-data-summarizer?
Analyzes CSV files, generates summary stats, and plots quick visualizations using Python and pandas.
Is csv-data-summarizer safe to install?
Review the Security Audits panel on this page before production use.