clean-code

To enforce clean code standards during AI refactoring, apply pragmatic principles like SRP, DRY, and KISS. Mandate pre-edit checks for dependencies and post-completion self-checks to ensure concise, maintainable code without over-engineering.

Best practices for function design involve creating small, focused functions with minimal arguments and no side effects. Promoting a clean structure with guard clauses and flat code prevents unnecessary complexity and improves readability.

Standardize naming conventions by ensuring clear, intent-revealing names for variables, functions, and constants. This approach enforces coding standards and makes the codebase more readable and maintainable for developers.

Yes, this approach integrates specific script verification for various specialist roles including frontend, backend, and security. It requires agents to read, summarize, and ask for confirmation before fixing script-identified issues.

Pre-edit checks are necessary to think about dependencies and impact before modifying any file. This process ensures solution-orientation, prevents errors, and maintains clean structure by evaluating the scope of changes beforehand.

The best way to handle unclear requirements is to follow AI interaction protocols that direct agents to ask for clarification. This ensures the resulting code adheres to YAGNI and avoids building unnecessary features.