foryourhealth111-pixel/engineering-features-for-machine-learning
bundled/skills/engineering-features-for-machine-learning/SKILL.md
Create, encode, transform, and select features before model fitting. Use when the user needs feature engineering decisions or implementation, not final training ownership or leakage auditing.
$ install --global
skillsauthnpx skillsauth add foryourhealth111-pixel/vco-skills-codex engineering-features-for-machine-learningInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Last scanned: Apr 26, 2026, 3:44 AM50.8s9 files scanned
SKILL.md
- name:
- engineering-features-for-machine-learning
- description:
- |
- allowed-tools:
- Read, Write, Edit, Grep, Glob, Bash(cmd:*)
- version:
- 1.0.0
- author:
- Jeremy Longshore <[email protected]>
- license:
- MIT
Feature Engineering Toolkit
Use this skill when the main question is how to improve or restructure the input features.
Overview
This skill covers feature creation, encoding, scaling coordination, and feature selection before the model is finalized.
When to Use This Skill
- Creating derived variables, interaction terms, bins, encodings, or date-based features
- Selecting or pruning features before training
- Reworking feature representations to fit model assumptions or data geometry
Not For / Boundaries
- Full training runs and benchmark ownership: use
training-machine-learning-models - Post-hoc interpretation of a trained model: use
feature-importance-analyzer - Leak checking across the preprocessing order: use
ml-data-leakage-guard
Typical Outputs
- Candidate feature set changes
- Implementation notes for encoders, scalers, and selectors
- Rationale for what to keep, drop, or combine
Related Skills
data-normalization-toolfor scaling-only questionsml-data-leakage-guardbefore accepting the engineered pipeline
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