Optimizes persistent memory retrieval through a 3-layer workflow that reduces token consumption by up to 90%.
Efficient Memory Search is a specialized skill designed for the agent-memory ecosystem, enforcing a disciplined three-step process for data retrieval. By first searching indexes, then exploring contextual timelines, and only fetching full details for filtered IDs, it prevents Claude from overwhelming the context window with unnecessary data. This skill is indispensable for developers managing long-term AI coding projects who need to maintain deep historical context without incurring massive token costs or losing performance.
Key Features
010 GitHub stars
02Context-aware timeline anchoring
03Hierarchical 3-layer search workflow
04Targeted observation fetching
0510x token savings via filtered retrieval
06Integration with persistent memory services
Use Cases
01Searching long-term project history for specific past implementation details
02Navigating chronological development logs to understand architectural evolution
03Reducing API costs and context window bloat during complex debugging sessions
