learning-capture

Automate learning capture by triggering this process on successful PR closeout, which checks for learning candidates and updates ADLC documentation accordingly. It requires access to PR metadata and a learning candidate manifest to function.

Conditional knowledge management works by capturing only verified and reusable learnings from ADLC operations. This mechanism ensures high-quality content by filtering out non-reusable data before creating or updating documentation.

Yes, you need a learning candidate manifest and access to PR metadata to document verified learnings. The automation relies on these inputs to identify and capture reusable knowledge during the pull request closeout process.

The best way to maintain ADLC documentation without manual effort is to automate documentation updates upon pull request closeout. This approach automatically captures verified, reusable learnings and integrates them directly into your docs.

Non-reusable learnings are filtered out during PR closeout automation. The conditionally triggered capture mechanism only processes verified, reusable knowledge, ensuring that only high-quality content is written to the documentation.