feature-engineering-and-leakage-checks v1.0.0A

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---
name: feature-engineering-and-leakage-checks
description: >
  Use this skill for feature creation, encoding, scaling, and explicit leakage checks across train, validation, and test boundaries.
version: 1.0.0
authors:
  - Marie-Lynne Block
tags:
  - data-science
  - [TODO]
---

## What this skill does

[TODO] Define the specific workflow this skill standardizes, including default libraries,
quality checks, and expected deliverables.

## When to use it

[TODO] List concrete user intents and trigger phrases that should activate this skill.

## Instructions

1. Clarify the objective, data assumptions, and success metrics.
2. Execute a leakage-safe and reproducible workflow for this skill domain.
3. Validate outputs with diagnostics, edge-case checks, and documented caveats.

## Output format

- A concise plan of action
- Executable code or commands
- Validation summary with assumptions and risks

## Examples

### Example 1 - baseline workflow

**Input:** User asks for help in feature-engineering-and-leakage-checks.
**Expected output:** A reproducible, validated workflow using the skill's core tools.

## Notes

- Prefer documented, stable APIs over experimental shortcuts.
- Record assumptions explicitly when data quality or labels are uncertain.