agentmemory-agents
How agentmemory wires into host coding agents via the connect command. Use when installing agentmemory into a specific agent, when asked which agents are…
Search agentmemory for past observations, sessions, and learnings about a topic using hybrid BM25 plus vector plus graph search. Use when the user says "recall", "what did we do about", "did we ever", "have we seen", or needs context from past sessions.
$ npx -y skills add rohitg00/agentmemory --skill recall --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/recallContext preview
The summary Claude sees to decide when to auto-load this skill.
Search agentmemory for past observations, sessions, and learnings about a topic using hybrid BM25 plus vector plus graph search. Use when the user says "recall", "what did we do about", "did we ever", "have we seen", or needs context from past sessions.
name: recall description: Search agentmemory for past observations, sessions, and learnings about a topic using hybrid BM25 plus vector plus graph search. Use when the user says "recall", "what did we do about", "did we ever", "have we seen", or needs context from past sessions. argument-hint: "[search query]" user-invocable: true
The user wants to recall past context about: $ARGUMENTS
memory_smart_search { "query": "jwt refresh token rotation", "limit": 10 }Expected output:
2 results across 2 sessions. [importance 8] decision · "Rotate refresh tokens on every use" (session 7f3a9c21) [importance 5] code · "limit.ts counts per-IP" (session b21d004e)
Only surface what the tool returned. Never fabricate an observation, a session id, or an importance score. If nothing comes back, say so.
1. Call `memory_smart_search` with the user's text as `query` and `limit: 10`. Pass `project` when the user scopes to a specific repo. 2. Group results by session. Records carry a provenance channel (`user`, `agent`, `tool`, `import`, `shared`); when results conflict, prefer `user` over `agent` inference, and flag `shared` records as another teammate's write. 3. For each observation show its type, title, and narrative. 4. Lead with the high-signal observations (importance >= 7). 5. If zero results, suggest 2-3 alternative search terms and stop. Do not guess.
WRONG: results are empty, so you write "We probably discussed token expiry last week" from assumption.
RIGHT: "No memories matched that query. Try `refresh token`, `session expiry`, or `auth rotation`."
See ../_shared/TROUBLESHOOTING.md if `memory_smart_search` is not available.
#1 Persistent memory for AI coding agents based on real-world benchmarks
Repo: rohitg00/agentmemory
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