What problem does it solve? When starting a new task or switching topics, an AI agent lacks awareness of past decisions, preferences, and lessons stored in long-term memory, forcing users to repeat context manually. ## Core Features & Use Cases - Parallel Memory Search: Runs 2-4 parallel queries against the mem0 memory store using different angles such as topic, people, and project references. - Deduplicated Context Block: Merges results by memory ID and outputs a compact block of up to 10 memories tagged by type (decisions, preferences, lessons). - Silent-on-Empty Behavior: Produces no output when no relevant memories exist, avoiding noise in the conversation. - Use Case: When a user says "what do we know about project X", the skill searches mem0 for related decisions and preferences and injects a summarized context block before work begins. ## Quick Start Ask the agent to load context for a topic, for example: "Load context for the payments migration project before we start planning."