clean-code

To enforce coding standards with AI agents, apply pragmatic principles like SRP, DRY, KISS, and YAGNI to ensure the generated code is concise, maintainable, and avoids over-engineering. This includes pre-edit impact analysis and post-completion verification.

The best way to refactor complex functions for maintainability is applying single responsibility and DRY principles, renaming variables for clarity, using guard clauses, and ensuring the function performs only one task with minimal arguments.

Prevent over-engineering by adhering to KISS and YAGNI principles, which discourage adding unnecessary features. Enforcing small, single-purpose functions and flat code structures also reduces complexity and technical debt.

Yes, AI coding assistants can check code quality by running mandatory self-verification scripts upon task completion. They verify goal completion, code correctness, and error-free execution while standardizing validation script output handling.

Running a pre-edit impact analysis before modifying code files ensures you consider all dependencies. It requires the AI agent to check related files and update dependent modules within the same task to prevent broken references.

To write concise, effective code, follow clear naming conventions for variables and constants, and apply function rules that promote small, single-purpose methods with minimal arguments and direct solution-orientation.