project-conventions

To align AI-generated code with repository conventions, document your specific code style, testing, and error handling patterns so the agent can reference them during implementation. This ensures generated code matches established project practices.

Refactoring introduces inconsistent error handling and file organization when repository standards are undocumented. Capturing project-specific patterns and anti-patterns provides codebase-aware guidance to keep structural changes aligned with existing practice.

The best way to check if new code matches project conventions is to review it against documented repository standards for code style, testing habits, and naming. This verifies changes follow expected patterns and avoids known anti-patterns before review.

Yes, you can use repository standards to guide AI implementation and refactoring tasks by providing convention-oriented guidance based on the project's patterns and examples. This keeps outputs aligned with established practice during codebase-aware decisions.

You need to document project conventions for code generation when adding new features or refactoring existing code that requires codebase-aware decisions. This ensures implementation, error handling, and file organization follow established practice.