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kaggle-eda

Detect distribution shifts, temporal patterns, target leakage, and analyze categorical features in Kaggle datasets.

1|Updated Jun 6, 2026
Search Tags:#feature engineering#data analysis#eda#kaggle#temporal analysis
One-click install
npx skills add https://github.com/OlixIgnacious/agent-skills --skill kaggle-eda
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: kaggle-eda
Source: https://github.com/OlixIgnacious/agent-skills/tree/main/.claude/skills/kaggle-eda
Command: npx skills add https://github.com/OlixIgnacious/agent-skills --skill kaggle-eda

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, matplotlib, seaborn, scipy, pypdf, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the need for more sophisticated Exploratory Data Analysis (EDA) in Kaggle competitions, helping to identify data shifts, temporal patterns, and potential leakage that could affect modeling decisions.

Core Features & Use Cases

  • Distribution Shift Detection: Identifies features with high distribution shifts between training and test sets.
  • Temporal Pattern Detection: Discovers temporal signals that may be present in time-series data.
  • Target Leakage Scan: Identifies features highly correlated with the target, suggesting potential leakage.
  • Categorical Feature Analysis: Inspects categorical features for high cardinality and unseen values.
  • GPU Accelerated EDA: Offers GPU-accelerated processing for faster analysis.

Quick Start

Run the skill with the command /kaggle-eda and it will analyze your data and generate a comprehensive report.

Frequently Asked Questions about kaggle-eda

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

FAQPage Schema
How do I detect distribution shifts between training and test datasets in Kaggle?▼

Target leakage occurs when features highly correlated with the target variable are identified during analysis. This skill scans your dataset to detect these features, preventing biased models and ensuring robust competition performance.

How do I analyze categorical features for high cardinality and unseen values?▼

Analyzing categorical features involves inspecting them for high cardinality and unseen values within the test set. This skill performs in-depth categorical feature analysis to highlight potential data issues before model training.

Can I use GPU acceleration for exploratory data analysis with pandas and numpy?▼

GPU acceleration is supported for exploratory data analysis to speed up processing. This skill benefits from GPU acceleration while utilizing pandas, numpy, scipy, and matplotlib for advanced data analysis workflows.

How do I identify temporal patterns in time-series data for Kaggle competitions?▼

Identifying temporal patterns requires discovering temporal signals present in time-series data. This skill detects temporal patterns automatically, helping you capture time-dependent features for improved modeling accuracy.

What is the best way to run automated exploratory data analysis for feature engineering?▼

Automated exploratory data analysis is executed by running the `/kaggle-eda` command. This skill generates a comprehensive report covering distribution shifts, temporal patterns, target leakage, and categorical feature analysis.

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