What problem does it solve? Valuable lessons learned during long agent sessions disappear when the conversation ends, forcing future agents to rediscover the same fixes, tool quirks, and workflow patterns. ## Core Features & Use Cases - Parallel multi-lens review: Spawns three reviewer subagents (judgment, tooling, divergent) over the active transcript, each surfacing durable learnings from a different angle. - Structured synthesis: A synthesizer subagent merges findings into an Accepted / Rejected / Backlog list with explicit routing to existing skills, description tuning, or new skill creation. - Approval-gated edits: Presents the full synthesis to the user before applying any change, routing substantive edits through the $skill-creator workflow. - Use Case: After a complex debugging session where the agent hit dead ends and found a working path, invoke reflect to capture the generalizable recipe as an edit to the relevant skill so the next agent avoids the same dead ends. ## Quick Start Say "reflect" after a complex task completes to mine the session transcript and route durable learnings into skill edits.