Transforms monolithic Python research code and notebooks into modular, production-ready package structures.
This skill facilitates the transition from experimental research code to maintainable Python software by reorganizing logic into a standardized project layout. It specializes in extracting core logic from Jupyter notebooks and scattered scripts into an importable 'src' directory while maintaining existing functionality. By separating concerns such as data loading, preprocessing, and modeling, it ensures your codebase follows industry best practices for modularity and clean import management without breaking existing workflows.
Key Features
01Migrates core logic from scripts/notebooks to a standardized src/ package structure
02Preserves algorithmic behavior while improving code architectural quality
03Organizes code into logical modules (data, modeling, evaluation, etc.)
04Standardizes package boundaries with appropriate __init__.py files
05Replaces duplicated logic in notebooks with clean module imports
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Use Cases
01Converting a complex Jupyter notebook into a reusable Python library
02Standardizing a messy repository into the recommended src-layout structure
03Transitioning experimental ML research code to a production-grade codebase
