Menu

Individual skill

ml-data-leakage-guard

0upvotes

Detects and prevents data leakage in machine learning and mathematical modeling. Use after ML tasks involving data cleaning, feature engineering, data augmentation, algorithm development, normalization, missing value imputation, dimensionality reduction, feature selection, or time series modeling. Checks if features/statistics would be available at prediction time.

bundled/skills/ml-data-leakage-guard/SKILL.md

Read pinned source

Source commit 8af6e230d3db0f3022236e67632be6d8b9323bf0

What was checked

Matches the observed repository HEAD
Yes
Scanner rules are current
No
Repository scan complete
No
Skill scan complete
No
Observed behavior
Not checked yet
Last checked
Nov 11, 2026

These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.

Install this skill

Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.

An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.

Repository context

Repository stars (not skill ratings)
3,816
Repository last updated
Nov 9, 2026
License
Apache-2.0

Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.

Files and excerpt

File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.

Browse files
Read excerpt

# ML Data Leakage Guard Skill

Automatically detects and prevents data leakage in machine learning workflows by verifying that all preprocessing steps, feature engineering, and statistical computations would be available at prediction time.

## When to Use This Skill

Use this skill after work involving:
- Data preprocessing (normalization, standardization, scaling)
- Missing value imputation
- Feature engineering and feature selection
- Dimensionality reduction (PCA, SVD, t-SNE)
- Target encoding or label encoding
- Time series feature construction
- Data augmentation strategies
Back to top