eda-analysis

What problem does it solve?

This Skill guides an agent through a six-phase exploratory data analysis pipeline that surfaces data-centric artifacts used in model training, schema design, and production drift monitoring.

Core Features & Use Cases

• A six-phase EDA workflow that ingests, profiles, analyzes univariate distributions, surfaces correlations, checks for leakage, and proposes feature candidates.
• Produces artifacts consumed by training (features.py), schema generation (schemas.py), and drift detection (baseline_distributions.parquet).
• Onboard new datasets into ML pipelines and validate data quality before modeling.

Quick Start

Ingest a dataset placed in data/raw and run the six-phase EDA pipeline to generate all artifacts and reports.