Can I use this for large CSV files?

Yes, it uses pandas for efficient data handling, though performance depends on your local environment's memory and Python configuration.

Does this skill require me to specify which charts I want to see?

No, the skill is designed to automatically identify and generate the most relevant visualizations based on the data structure it detects.

How does the skill handle different types of data like sales or surveys?

The skill inspects your column headers and data types to determine the context, then applies specific logic for time-series, categorical, or financial data accordingly.

Is there any manual configuration required to get an analysis?

No. The skill follows a 'Critical Behavior Requirement' to immediately run the analysis and present results without asking for user permission or choices.

What Python dependencies are needed for this skill?

It requires Python 3.8 or higher, along with the pandas, matplotlib, and seaborn libraries for data processing and visualization.

CSV Data Summarizer

Name: CSV Data Summarizer
Author: lifangda

bylifangda

•

데이터 과학 및 ML

Automatically analyzes CSV files to generate comprehensive statistical summaries and tailored visualizations without requiring user prompts.

소개

The CSV Data Summarizer is a specialized Claude Code Skill designed for immediate, autonomous data exploration. Upon detecting a CSV file, it bypasses traditional back-and-forth interactions by automatically inspecting the data structure, identifying column types, and performing context-aware analyses—ranging from time-series trends to demographic distributions. It intelligently selects the most appropriate visualizations, such as correlation heatmaps or histograms, and delivers a complete report with actionable insights in a single step, making it an essential tool for rapid data profiling and exploratory analysis.

주요 기능

Zero-interaction workflow that delivers full analysis immediately without asking questions
Adaptive analysis that detects data types (Sales, Finance, Survey, etc.) and adjusts insights
Automated statistical profiling including distribution, missing values, and key metrics
Intelligent visualization selection using Matplotlib and Seaborn based on column relevancy
Comprehensive data quality assessment and structural overview in a single response
16 GitHub stars

사용 사례

Generating instant business insights from sales or transactional CSV exports without manual coding
Visualizing trends and correlations in experimental data for quick hypothesis testing
Rapidly profiling a new dataset to understand quality, structure, and missing values

소개

주요 기능

Zero-interaction workflow that delivers full analysis immediately without asking questions
Adaptive analysis that detects data types (Sales, Finance, Survey, etc.) and adjusts insights
Automated statistical profiling including distribution, missing values, and key metrics
Intelligent visualization selection using Matplotlib and Seaborn based on column relevancy
Comprehensive data quality assessment and structural overview in a single response
16 GitHub stars

사용 사례

Generating instant business insights from sales or transactional CSV exports without manual coding
Visualizing trends and correlations in experimental data for quick hypothesis testing
Rapidly profiling a new dataset to understand quality, structure, and missing values