learn

To extract durable facts from a conversation for knowledge management, trigger the skill to review your current session. It reflects on the dialogue, identifies key decisions, and saves long-lasting facts to persistent memory for future use.

Self-reflection in conversation analysis reviews ongoing dialogue to evaluate what went well, what went wrong, and the decisions made. It identifies user preferences by extracting specific, durable facts from the session and saving them to memory.

Yes, you can use this to generate session summaries for project knowledge management. It analyzes your conversation history to extract relevant learnings and durable facts, saving them directly into persistent memory for later retrieval.

To manage memory entries under constraints, you can add, update, or remove facts based on their priority. This ensures your persistent memory remains optimized and only retains the most relevant durable facts for future sessions.

Yes, fact extraction works without setting up external dependencies. The skill operates independently using internal scripts to review conversations, reflect on the content, and save extracted durable facts directly to persistent memory.

You should not use automated conversation analysis for saving durable facts when a session contains only transient information. If the dialogue lacks long-lasting decisions or stable user preferences, extracting facts provides little value for persistent memory.