session-intelligence-harvester

Encoding session learnings into permanent organizational intelligence involves parsing productive session outcomes to extract corrections, detected drift, and new patterns, then routing them to RII components like CLAUDE.md and Constitution files for durable persistence.

Preventing knowledge drift after a Claude workflow requires harvesting corrections and new patterns from the session and encoding them across multiple RII target files, committing the changes to ensure end-to-end traceability and prevent repeated mistakes.

Harvesting session outcomes is needed after productive sessions where corrections were made, drift was detected, or new patterns emerged, ensuring these discoveries are classified and formally integrated into your organizational knowledge infrastructure.

Applying cross-file changes for session analysis updates involves routing extracted learnings to the appropriate RII components and related skill files, applying the modifications across all targets, and making a commit to establish formal traceability.

This session harvesting approach requires no external dependencies or components, operating independently to parse session outcomes, classify learnings, and route updates to existing RII files like CLAUDE.md for immediate integration.

Session harvesting supports change management by applying extracted corrections and patterns across multiple target files simultaneously, satisfying formal update workflows and ensuring all related RII components reflect the new organizational learnings.