About
This skill streamlines the data preparation phase of machine learning projects by automatically dividing raw datasets into optimized subsets. It analyzes user requirements for split ratios, generates production-ready Python code using standard libraries, and executes the partitioning to ensure data integrity. By handling randomization automatically, it helps developers prevent data leakage and selection bias, facilitating more robust model evaluation and faster ML experiment iteration directly within the Claude Code environment.
