continuous-learning

You can extract patterns from coding sessions by running an analysis that categorizes learnings into Corrections, Successful Approaches, and Anti-Patterns. The output is a structured knowledge base with confidence scores ready to append to your documentation.

The best way to capture lessons from code reviews is to categorize them into Corrections, Successful Approaches, and Anti-Patterns. This generates a confidence-scored knowledge base that ensures consistent onboarding and future coding practices.

Yes, you can generate a CLAUDE.md knowledge base by summarizing key learnings from your coding sessions. The extracted patterns are formatted with confidence scores and structured to append directly to your existing documentation.

Confidence scoring assigns a reliability metric to each extracted learning from your coding sessions. This structures your knowledge base so you can prioritize high-confidence corrections and successful approaches over less certain anti-patterns.

You should categorize learnings into corrections, successful approaches, and anti-patterns after completing coding sessions, code reviews, or pair programming. This organization turns raw session data into a structured, reusable knowledge base.