aif-evolve

To improve AI skills by analyzing past codebase patches, you can use a skill self-improvement process that consumes accumulated patches, identifies recurring issues like missing null checks, and generates actionable rules to prevent future errors.

Skill self-improvement in an AI Factory workflow is the process of analyzing project context and codebase patterns to rectify recurring issues, generating project-specific rules stored in a dedicated context directory to enhance skill performance over time.

You generate project-specific rules to prevent recurring coding errors by performing gap analysis on accumulated patches and codebase patterns, automatically creating new contextual guards that enforce checks for identified pitfalls.

Yes, you can automatically add missing guards to your codebase analysis skills by detecting recurring problems through incremental patch consumption and generating contextual rules that enforce these guards for future operations.

Relying on accumulated patches for skill improvement is limited by the existing patch history; if past codebase patterns lack diversity or fail to capture specific edge cases, the generated contextual rules may not cover all potential future errors.