csv-data-summarizer

Name: csv-data-summarizer
Availability: InStock
Author: franklee16

Profile CSV datasets with statistics and visualizations for each column type.

203|27|Updated Apr 19, 2026

Search Tags:#python #visualization #pandas #csv #missing values #data summarization

Ready to run

npx skills add https://github.com/franklee16/academic-research-skills --skill csv-data-summarizer-franklee16

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: csv-data-summarizer
Source: https://github.com/franklee16/academic-research-skills/tree/main/data-analysis/csv-data-summarizer
Command: npx skills add https://github.com/franklee16/academic-research-skills --skill csv-data-summarizer-franklee16

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, matplotlib, seaborn.

What problem does it solve?

This Skill removes the tedious effort of manually inspecting spreadsheets by producing a complete overview of a CSV dataset, including data quality checks, summary statistics, and relevant visualizations.

Core Features & Use Cases

Comprehensive dataset profiling: Reports row/column counts, column names, and inferred data types.
Data quality insights: Calculates missing-value totals and missing-by-column breakdowns.
Statistics and visualizations for key column types: Produces numeric summaries (including correlations and heatmaps when applicable), time-series plots when date/time columns exist, and distribution charts for numeric and categorical columns.

Quick Start

Upload your CSV file and ask Claude to run the csv-data-summarizer to produce a full summary with visualizations immediately.

Frequently Asked Questions about csv-data-summarizer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I automatically summarize a CSV file and generate visualizations?▼

To summarize a CSV file and generate visualizations, upload the dataset and run the tool to profile structure, compute descriptive statistics, and plot numeric, categorical, and date/time columns automatically using pandas and matplotlib.

What is the best way to check for missing values in a CSV dataset?▼

Checking missing values in a CSV dataset involves calculating missing-value totals and missing-by-column breakdowns, which this approach handles automatically alongside structure profiling and data type inference during ingestion.

Can I generate correlation heatmaps and time-series plots from CSV data without manual spreadsheet work?▼

Generating correlation heatmaps and time-series plots from CSV data without spreadsheets is possible; the tool conditionally creates correlation heatmaps for numeric columns and time-series plots when date/time columns exist.

Does pandas-based CSV profiling support categorical column distribution charts?▼

Pandas-based CSV profiling supports categorical column distribution charts by inferring data types and producing relevant distribution visualizations for both numeric and categorical columns in one run.

How do I get descriptive statistics for all columns in a CSV upload?▼

Getting descriptive statistics for all columns in a CSV upload requires profiling the dataset structure and computing summaries, which this method delivers immediately alongside data quality insights and visualizations.

What are the limitations of automated data summarization for CSV files?▼

Automated data summarization for CSV files is limited to datasets compatible with pandas ingestion, requiring matplotlib and seaborn dependencies to render numeric, categorical, and date/time visualizations successfully.