What problem does it solve? AI agents lose context between sessions, forcing teams to repeatedly re-explain architecture decisions, patterns, and project knowledge. This Skill provides a persistent, searchable memory bank that syncs with project documentation so knowledge survives across conversations. ## Core Features & Use Cases - Memory Search: Query stored knowledge by text, type, or tags using the memory_search MCP tool. - Memory Write & Read: Record architecture decisions, patterns, and notes with memory_write, then retrieve them by key with memory_read. - Usage Analytics & Dashboard: Inspect memory statistics via memory_stats and visualize usage in a standalone dashboard at localhost:3333. - Use Case: After deciding on an authentication approach, save it with memory_write as a decision entry; weeks later, any agent session can retrieve it instantly with memory_search instead of re-deriving the rationale. ## Quick Start Clone the agentMemory repository, run npm install and npm run compile, then start the MCP server with your project ID and workspace path to begin storing and searching memories.