clean-code

To enforce coding standards for AI-generated code, apply pragmatic rules that enforce the Single Responsibility Principle, DRY, and KISS. This ensures AI agents produce concise, maintainable code without unnecessary comments or over-engineering.

Refactoring legacy code for maintainability involves simplifying complex functions, renaming ambiguous variables, and ensuring each component has a clear purpose. Applying pragmatic coding standards promotes code reuse and avoids premature feature building.

To stop AI agents from over-engineering code, enforce pragmatic coding standards that guide towards the simplest effective solution. This includes mandatory verification script execution and self-check mechanisms to address complex function design.

Yes, this code quality guidance requires Python as a dependency to run its mandatory verification scripts. The standards themselves apply to general coding practices, but the automated enforcement mechanism relies on the Python environment.

You should reconsider strict clean code principles when rapid prototyping requires immediate functionality over maintainability. However, for long-term software engineering, applying these standards prevents code duplication and ensures each function does one thing well.