clean-code

To enforce clean code standards in AI-generated code, apply pragmatic coding principles like SRP, DRY, KISS, and YAGNI. This ensures the generated code remains concise, direct, and avoids unnecessary complexity or comments for better maintainability.

The best way to refactor code to avoid over-engineering is to follow pragmatic principles like KISS and YAGNI. This approach focuses on directness, clear naming conventions, and guard clauses to reduce unnecessary complexity and improve code structure.

To verify code quality before completing a refactoring task, run pre-edit verification checks and execute validation scripts. This process ensures the objective is met, functionality is intact, and edge cases or errors are resolved before finalizing.

This clean code skill applies across programming languages by enforcing universal pragmatic coding standards rather than framework-specific rules. It guides AI agents on general function design, naming conventions, and code structure to ensure quality.

AI-generated code often includes unnecessary comments and complexity due to a lack of enforced pragmatic coding standards. By applying directives for conciseness, directness, and anti-pattern avoidance, AI agents can produce more maintainable and efficient code.

A limitation of using automated coding standards is that the AI must still perform pre-edit verification to prevent breaking changes. While standards guide naming and structure, manual final checks and script execution are required to ensure true functionality.