What Python libraries are used for the summary?

The skill utilizes pandas for data processing, and matplotlib and seaborn for generating professional-grade visualizations.

Does it support time-series analysis?

Absolutely. If the skill detects date or timestamp columns, it will automatically generate temporal trend plots and activity distributions over time.

Do I need to tell Claude how to analyze my data?

No. This skill is designed to be fully autonomous. Once a CSV is provided, it automatically detects the data structure and generates all relevant analyses and charts without asking for further instructions.

Can it handle different types of data like finance or healthcare?

Yes, the skill inspects your columns to identify if the data is related to sales, customers, finance, or operations, and tailors the insights and plots accordingly.

CSV Data Summarizer

Name: CSV Data Summarizer
Author: Krosebrook

byKrosebrook

•

Data Science & ML

Analyzes CSV files to provide immediate statistical summaries and automated data visualizations using Python and pandas.

About

This Skill enables Claude to perform instant exploratory data analysis (EDA) on any CSV file without requiring manual prompts or follow-up questions. It intelligently adapts to your data's specific domain—whether it's e-commerce, finance, healthcare, or web analytics—to generate context-aware statistical reports, missing value assessments, and relevant visualizations like time-series trends and correlation heatmaps. By automating the data inspection and plotting process, it transforms raw tabular files into actionable insights in a single step.

Key Features

1 GitHub stars
Contextual visualization mapping (Time-series, Heatmaps, Histograms)
Automatic missing data and quality analysis
Zero-instruction execution for immediate results
Automated data type and domain detection
Comprehensive statistical summary generation

Use Cases

Generating instant sales and revenue trend reports from raw transaction CSVs
Auditing data quality and identifying missing values in large datasets
Performing rapid customer segmentation and demographic distribution analysis

About

Key Features

1 GitHub stars
Contextual visualization mapping (Time-series, Heatmaps, Histograms)
Automatic missing data and quality analysis
Zero-instruction execution for immediate results
Automated data type and domain detection
Comprehensive statistical summary generation

Use Cases

Generating instant sales and revenue trend reports from raw transaction CSVs
Auditing data quality and identifying missing values in large datasets
Performing rapid customer segmentation and demographic distribution analysis