explore-ml-data

Automate exploratory data analysis and generate narrative and HTML reports.

Updated Apr 19, 2026
One-click install
npx skills add https://github.com/vathymut/copilot-skills --skill explore-ml-data
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: explore-ml-data
Source: https://github.com/vathymut/copilot-skills/tree/main/.github/skills/explore-ml-data
Command: npx skills add https://github.com/vathymut/copilot-skills --skill explore-ml-data

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires skrub, pandas, polars, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps users gain a comprehensive understanding of their datasets before building machine learning models, enabling informed decisions based on real dataset insights.

Core Features & Use Cases

  • Exploratory Data Analysis (EDA): Automates the process of understanding dataset characteristics, including class balance, datetime columns, missingness, and cardinality.
  • Documentation Generation: Generates a narrative report and interactive HTML reports for each table in the dataset.
  • Integration: Provides a JOURNAL section that summarizes EDA findings and serves as a reference for subsequent model design decisions.

Quick Start

Run the 'explore-ml-data' skill to perform EDA on your dataset.

Frequently Asked Questions about explore-ml-data

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

FAQPage Schema
How do I automate exploratory data analysis on a pandas dataset before model design?

Automated exploratory data analysis identifies dataset characteristics like class balance, missingness, and cardinality. This generates narrative and interactive HTML reports to inform machine learning model design decisions.

What's the best way to understand dataset characteristics like missingness and cardinality?

Understanding dataset characteristics requires exploratory data analysis to evaluate class balance, datetime columns, missingness, and cardinality. This yields a narrative report and interactive HTML summary for each table.

Does automated EDA work with polars dataframes for machine learning preparation?

Automated EDA works with polars dataframes and requires either pandas or polars alongside skrub for data analysis. It processes dataset tables to produce comprehensive EDA reports for machine learning preparation.

Can I generate an interactive HTML report for each table in my dataset?

You can generate an interactive HTML report and narrative documentation for each table in your dataset. The EDA output also includes a JOURNAL section summarizing findings for subsequent model design.

When do I need exploratory data analysis before building machine learning models?

Exploratory data analysis is needed before building machine learning models to ensure informed decisions based on real dataset insights. It automates understanding of key dataset characteristics to prepare for model design.

What limitations should I expect when automating dataset analysis with skrub?

Automating dataset analysis with skrub requires pandas or polars dependencies for data processing. The generated EDA reports focus on structural dataset characteristics like class balance and missingness rather than model performance evaluation.