clean-code

To enforce clean code standards during AI development, apply rules that guide AI agents toward conciseness, maintainability, and efficiency. These standards ensure adherence to SRP, DRY, KISS, and YAGNI principles, preventing unnecessary complexity and technical debt.

Best practices for refactoring code to improve maintainability involve creating small, single-purpose functions with limited arguments. You should use clear naming conventions, implement guard clauses, prefer flat code structures, and choose composition over deep nesting to reduce complexity.

To prevent common coding anti-patterns when generating scripts, utilize defined AI coding styles that list frequent pitfalls alongside their corrections. This approach promotes a direct, solution-oriented methodology, ensuring generated code avoids deep nesting and unnecessary variables.

Yes, you can use coding standards to define how validation scripts are executed and how their results are summarized. This includes mandating thorough post-completion self-checks and pre-edit impact considerations to verify code quality before finalizing tasks.

You need to apply pre-edit checks for code structure modifications whenever altering existing files. These checks ensure you consider the impact before modifying code, enforcing a direct, solution-oriented approach to coding tasks and maintaining structural integrity.