What problem does it solve? AI agents lose context between sessions, forcing users to repeat preferences, project details, and past decisions. This Skill provides a persistent two-layer memory system so important facts and event history survive across conversations. ## Core Features & Use Cases - Long-Term Fact Storage: Records user preferences, project context, and relationships in MEMORY.md, which is always loaded into the agent's context. - Searchable Event Log: Appends timestamped entries to HISTORY.md and retrieves them with grep-style searches or in-memory filtering, scaled to file size. - Auto-Consolidation: Automatically summarizes old conversations into HISTORY.md and extracts long-term facts into MEMORY.md when sessions grow large. - Use Case: A user mentions "I prefer dark mode" and "Alice is the project lead" in one session; weeks later the agent recalls both facts without being reminded. ## Quick Start Ask the agent to remember a fact such as "remember that our API uses OAuth2" and later ask it to search history for when that decision was made.