self-improvement

You can log command failures and API errors by capturing the error context and recording it as structured markdown files within a .learnings/ directory to facilitate subsequent review.

Continuous improvement for AI workflows involves capturing user corrections and knowledge gaps. Error logging works by recording command failures and unexpected behaviors into structured markdown files for later review.

You promote captured learnings to project memory files like CLAUDE.md by reviewing the structured logs in your .learnings/ directory and facilitating the promotion of valuable insights into project memory.

Yes, you can extract reusable skills from recurring knowledge gaps and user corrections by using a provided workflow to turn recurring learnings into reusable skills, accelerating development.

No, tracking feature requests and documenting missing capabilities requires no external dependencies, relying solely on structured logging into markdown files within a local .learnings/ directory.

You should not use a .learnings/ directory for debugging and knowledge management if your scenario lacks recurring command failures, user corrections, or knowledge gaps, as it is designed specifically for continuous AI agent improvement.