Automate proportional cleanup during incremental Python edits.
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When editing Python code, this Skill enables proportional cleanup focused on low-risk refactors, naming improvements, and dead-code removal to improve readability and maintainability without changing behavior.
Run a small, proportional refactor to improve naming, remove dead code, and add clear comments in the touched Python module.
High-intent search queries and answers about installing and using this skill.
Proportional cleanup applies low-risk refactors, naming improvements, and dead-code removal to Python modules during small edits. Use it when touching existing modules to reduce technical debt without changing behavior.
Run a targeted, incremental cleanup on the touched Python module to improve naming, remove dead code, and add clear comments. This non-invasive approach preserves functionality while aligning with project conventions.
No, proportional cleanup is designed for small-to-medium refactors and incremental edits. It avoids broad rewrites, ensuring changes are non-invasive and preserve existing behavior across touched Python modules.
Apply proportional cleanup during edits to perform safe, incremental improvements. This method avoids broad rewrites and explicitly preserves functionality while updating comments and structure in existing Python modules.
No dependencies are required. The cleanup process operates directly on existing Python modules, applying naming improvements and dead-code removal while aligning with Python project conventions and best practices.
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