project-conventions

To standardize coding standards for an AI agent, you can codify your repository conventions into a structured document. This captures naming, error handling, and file layout patterns so agent outputs match team expectations.

Documenting repository conventions for onboarding involves structuring team patterns, testing practices, and code style into clear do-and-why guidance with explicit anti-patterns to steer generation away from common mistakes.

Yes, you can use existing README files to generate project conventions. You can ask an AI to turn your current README and testing docs into a clean conventions document with filled-in sections for code style and file structure.

Coding standards reduce inconsistent AI code generation by providing explicit patterns and anti-patterns. This steers the agent away from common mistakes and ensures outputs reflect how your project is actually maintained and reviewed.

Standardizing testing practices includes YAML frontmatter metadata for discovery. This metadata accompanies an instructional body defining patterns and anti-patterns required for consistent outputs during maintenance tasks.

You need to codify error handling and code style when onboarding new members or using agent-driven code generation. This ensures consistent outputs and aligns generated code with your team's quality expectations.