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preprocessing-data-with-automated-pipelines

Design and implement repeatable workflows that clean, encode, transform, and validate raw data for machine learning. Get a preprocessing plan or implementation outline, with clear steps and guidance on where further checks are needed.

OriginalShow the original description

Design and implement repeatable preprocessing pipelines for cleaning, encoding, transforming, and validating ML input data.

See how to install

Good for

  • Preparing raw data for machine learning
  • Automating recurring data cleaning
  • Planning data extraction, transformation, and loading
  • Organizing input validation steps

Files included (9)

  • SKILL.md1.5 KB
  • assets/example_data.csv1.3 KB
  • assets/README.md309 B
  • references/README.md437 B
  • scripts/handle_errors.py10.1 KB
  • scripts/pipeline.py11.5 KB
  • scripts/README.md426 B
  • scripts/transform_data.py13.1 KB
  • scripts/validate_data.py7.5 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

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

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