What problem does it solve? Valuable operational learning from conversations—decisions, root causes, gotchas, and reasoning traces—is lost when a session ends. This Skill turns a transcript or log into durable, queryable facts stored in the Memory graph so agents stop repeating old mistakes after context resets. ## Core Features & Use Cases - LLM-Backed Extraction: Reads a conversation or log from a file or stdin and asks the configured AI provider to identify durable facts. - Graph Upsert: Writes extracted knowledge as INFERRED nodes and edges into the Memory graph, reporting how many facts and edges were created. - Verification via Recall: Confirms the new facts are discoverable by running a targeted recall query after extraction. - Use Case: After a long debugging session, pipe the session transcript into the extract command so the root cause and the fix constraint are preserved as graph facts that future sessions can recall. ## Quick Start Extract the durable decisions and gotchas from my session transcript file debug-session.log into the Memory graph, then verify them with a recall query.