What problem does it solve? Valuable insights discovered during a work session—confirmed facts, resolved errors, architectural decisions—are usually lost when the session ends. This Skill captures those learnings at session end and persists them into a tracked lesson memory so future sessions can reuse them. ## Core Features & Use Cases - Session Learning Extraction: Identifies new facts, patterns, decisions, resolved errors, and skill updates from the just-finished session and formats them as dated entries. - Approval-Gated Writes: Proposes a minimal, additive-only diff and waits for explicit user approval (approve/edit/skip) before writing or committing anything. - Idempotent Capture: Checks for duplicate topics, never overwrites existing entries, and includes a version guard to skip redundant writes. - Use Case: After finishing a debugging session that uncovered a root cause and fix pattern, invoke the skill to record the lesson into the PT .agent memory and commit it to git for future reuse. ## Quick Start Ask the AI to run the self-improve skill at the end of your session to review what was learned and propose a lesson entry for your approval.