preprocessing-data-with-automated-pipelines

name	preprocessing-data-with-automated-pipelines
description	Design and implement repeatable preprocessing pipelines for cleaning, encoding, transforming, and validating ML input data.
allowed-tools	Read, Write, Edit, Grep, Glob, Bash(cmd:*)
version	1.0.0
author	Jeremy Longshore <[e-mail]>
license	MIT

Data Preprocessing Pipeline

Positioning

Use this skill as the direct owner for ML input-preparation pipelines.

It covers preprocessing-heavy tasks where the requested deliverable is a repeatable pipeline for cleaning, encoding, transforming, and validating input data.

When to Use

Use this skill when:

• Prepare raw data for machine learning models.
• Automate data cleaning and transformation processes.
• Implement a robust ETL (Extract, Transform, Load) pipeline.

Not For / Boundaries

• Whole-task ML ownership: use scikit-learn or ml-pipeline-workflow
• Leakage and prediction-time auditing: use ml-data-leakage-guard
• Grouped scientific preprocessing with stronger methodological constraints: use scientific-data-preprocessing

Typical Outputs

• A preprocessing pipeline plan or implementation sketch
• Clear sequencing for clean, encode, transform, and validate steps
• Notes that identify where leakage review, training, or evaluation should be run next

Related Skills

• ml-data-leakage-guard before trusting fitted preprocessing steps
• splitting-datasets when the next narrow problem is partition strategy