eda

Name: eda
Availability: InStock
Author: kamalyunus

Profile tabular data and generate an HTML EDA report.

2|Updated Feb 15, 2026

Search Tags:#profiling #drift #eda #data-quality #leakage #modeling-readiness

Ready to run

npx skills add https://github.com/Kamalyunus/claude-skills --skill eda-kamalyunus

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: eda
Source: https://github.com/Kamalyunus/claude-skills/tree/main/eda
Command: npx skills add https://github.com/Kamalyunus/claude-skills --skill eda-kamalyunus

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The EDA skill helps users explore, profile, and understand tabular data for data science or ML tasks, turning raw data into actionable insights that inform modeling decisions.

Core Features & Use Cases

EDA and data profiling
Data quality checks and anomaly detection
Assess feature relationships and distributions to guide modeling and cleaning

Quick Start

Upload a tabular dataset (CSV, Excel, Parquet) and ask me to analyze its structure, quality, and relationships to generate an actionable EDA report.

Frequently Asked Questions about eda

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

FAQPage Schema

How do I perform exploratory data analysis on a CSV file before modeling?▼

To perform exploratory data analysis on a CSV file, you upload the dataset and request a structural, quality, and relationship assessment. The skill profiles univariate distributions, detects anomalies, and outputs an actionable HTML report.

What is data leakage detection and how does it work during data profiling?▼

Data leakage detection during data profiling identifies features that improperly reveal the target variable, compromising model validity. This skill scans tabular datasets to uncover missingness mechanisms and target leakage, ensuring modeling readiness before training.

Can I run data quality checks and anomaly detection on Excel or Parquet datasets?▼

Yes, you can run data quality checks and anomaly detection on Excel or Parquet datasets. The skill accepts these formats natively, auditing missingness mechanisms and identifying anomalies to generate a comprehensive data understanding report.

How do I analyze data drift and feature correlations in tabular data?▼

To analyze data drift and feature correlations in tabular data, the skill evaluates distribution shifts and inter-feature relationships. It synthesizes these findings into a modeling roadmap, highlighting necessary cleaning steps and structural insights.

When do I need univariate profiling and target analysis for machine learning preparation?▼

You need univariate profiling and target analysis for machine learning preparation when transitioning from raw data to model-ready features. This skill maps feature distributions and target relationships, flagging quality issues to guide your cleaning strategy.