simplify

To review generated Python modeling code for unnecessary complexity, analyze the src/*.py files for duplication, inefficient loops, and overly defensive exception handling, then generate a markdown report with non-modifying recommendations.

The best way to check modeling pipeline code for reuse and vectorization is to perform a post-coding quality review that inspects the produced src/*.py files for inefficiencies and naming inconsistencies against the modeling plan.

A non-modifying code review works by analyzing the source files to identify code smells like exception swallowing and mismatches with the modeling plan, outputting a recommendation artifact without changing the actual behavior of the code.

You need to run a code quality review in a modeling pipeline after the coding stage and before packaging, ensuring the generated src/*.py files are maintainable and behavior-consistent before they are finalized.

Yes, you can detect exception swallowing and overly defensive error handling in Python by analyzing the source files for these specific code smells and documenting them in a simplify report.

A key limitation when refactoring modeling code based on automated recommendations is that the review process itself outputs suggestions only and does not change code behavior, requiring you to apply the refactoring manually.