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name: splitting-datasets description: | Split datasets into training, validation, and test partitions with the right stratification and temporal rules. Use as a narrow preprocessing helper once the broader ML workflow is already chosen, not as the main route owner for an end-to-end ML task. allowed-tools: Read, Write, Edit, Grep, Glob, Bash(cmd:*) version: 1.0.0 author: Jeremy Longshore jeremy@intentsolutions.io license: MIT

Dataset Splitter

Positioning

Treat this skill as a narrow helper for partition strategy.

When to Use

Use this skill when:

• Prepare a dataset for machine learning model training.
• Create training, validation, and testing sets.
• Partition data to evaluate model performance.

Not For / Boundaries

• Full preprocessing-pipeline ownership: use preprocessing-data-with-automated-pipelines
• Leakage audits and prediction-time checks: use ml-data-leakage-guard
• Model training and tuning after the split: use scikit-learn

Typical Outputs

• Partition strategy with ratios, random seeds, and stratification rules
• Notes on temporal or grouped split constraints
• Handoff guidance for leakage review and downstream training

Related Skills

• preprocessing-data-with-automated-pipelines for the broader preprocessing sequence
• ml-data-leakage-guard to verify the split does not leak future or test information