lessons-learned

Automated failure analysis prevents repeated mistakes by extracting structured failure records from case exports and consolidating them into a centralized lessons_learned.json knowledge base. It runs after case completion or pipeline failures requiring human intervention.

Consolidating structured failure records into a centralized knowledge base is best handled by reading case exports and updating a lessons_learned.json file. This process adds new entries while avoiding duplicates to support anti-pattern discovery and knowledge base expansion.

You need to update your lessons learned knowledge base after every case completion or pipeline failure requiring human intervention. This ensures structured failure records are ingested and the lessons_learned.json file is refreshed accordingly.

Automated case review supports anti-pattern discovery by reading structured failure records and related reports from cases/exports. It updates cases/config/lessons_learned.json to expand your knowledge base and codify recurring issues.

The knowledge base update process reads only structured failure records and related reports from cases/exports. It specifically updates cases/config/lessons_learned.json and does not process unstructured failure data.

To avoid duplicate entries when capturing lessons learned, the skill reads structured failure records from case exports and updates lessons_learned.json with new entries while actively preventing duplication during the ingestion process.