/preprocessing-data-with-automated-pipelines
Design and implement repeatable preprocessing pipelines for cleaning, encoding, transforming, and validating ML input data.
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- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
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/preprocessing-data-with-automated-pipelines
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Design and implement repeatable preprocessing pipelines for cleaning, encoding, transforming, and validating ML input data.
SKILL.md
preprocessing-data-with-automated-pipelines.SKILL.mdname: 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 <jeremy@intentsolutions.io>
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
Read more
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 <jeremy@intentsolutions.io> 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
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