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From agent-memory
3-LAYER WORKFLOW (ALWAYS FOLLOW): 1. search(query) → Get index with IDs (~50-100 tokens/result) 2. timeline(anchor=ID) → Get context around interesting results 3. get_observations([IDs]) → Fetch full details ONLY for filtered IDs NEVER fetch full details without filtering first. 10x token savings.
How this skill is triggered — by the user, by Claude, or both
Slash command
/agent-memory:memory_search_guideThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
3-LAYER WORKFLOW (ALWAYS FOLLOW):\n1. search(query) → Get index with IDs (~50-100 tokens/result)\n2. timeline(anchor=ID) → Get context around interesting results\n3. get_observations([IDs]) → Fetch full details ONLY for filtered IDs\nNEVER fetch full details without filtering first. 10x token savings.
3-LAYER WORKFLOW (ALWAYS FOLLOW):\n1. search(query) → Get index with IDs (~50-100 tokens/result)\n2. timeline(anchor=ID) → Get context around interesting results\n3. get_observations([IDs]) → Fetch full details ONLY for filtered IDs\nNEVER fetch full details without filtering first. 10x token savings.
When invoked with /memory_search_guide, call the memory_search_guide MCP tool (server: agent-memory).
memory_search_guide()
npx claudepluginhub metazen11/agent-memory --plugin agent-memoryProvides behavioral guidelines to reduce common LLM coding mistakes, focusing on simplicity, surgical changes, assumption surfacing, and verifiable success criteria.
Searches, retrieves, and installs Agent Skills from prompts.chat registry using MCP tools like search_skills and get_skill. Activates for finding skills, browsing catalogs, or extending Claude.