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splitting-datasets

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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.

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How to use this skill

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  2. Copy the prompt below and paste it into your agent.
  3. Review the proposed files and risks before you approve installation.
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I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/foryourhealth111-pixel/Vibe-Skills/blob/HEAD/bundled/skills/splitting-datasets/SKILL.md

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First, summarize what it does, its dependencies, license status if identifiable, and any risks. Show the exact files you propose to add under .agents/skills/splitting-datasets/. Do not write files or run scripts until I approve.

After I approve, install the complete skill folder, including required referenced files, into that project location. Verify it is discoverable, then tell me its actual invocation name and how to use it. Do not claim it is installed until you have verified it.

Copying this prompt does not install or run the skill. Review third-party files before use. Codex skill guide

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