clean-code

To enforce clean code standards during AI refactoring, apply guidelines focusing on SRP, DRY, KISS, and YAGNI. This ensures functions remain small, intent-revealing, and free of unnecessary complexity or side effects during automated modifications.

Best practices for preventing over-engineering involve mandating pragmatic coding standards like KISS and YAGNI. This directs the AI agent to prioritize conciseness, avoid unnecessary comments, and strictly limit function arguments and nested structures.

Automate code quality checks by running defined validation scripts for linting and type coverage. The skill mandates post-task self-verification and script execution to validate code correctness, structure, and internationalization after modifications.

No, this clean code skill requires Python installed in your environment. Python is a necessary dependency because the skill includes specific script execution mandates for linting, type coverage validation, and post-task verification.

Before modifying files to ensure clean code structure, an AI agent must perform pre-edit checks. This involves thinking about dependencies, analyzing impact, and planning guard clauses and flat nesting before making any code changes.

To handle unclear requirements during AI refactoring, use defined AI interaction patterns. The skill provides specific guidelines directing agents on how to manage ambiguous user requests, bugs, and unclear requirements pragmatically without over-engineering.