/recall
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.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/recall
Context 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.
SKILL.md
recall.SKILL.mdname: 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
Quick start
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)
Why
Only surface what the tool returned. Never fabricate an observation, a session id, or an importance score. If nothing comes back, say so.
Workflow
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. 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.
Anti-patterns
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`."
Checklist
- Every observation shown came from the tool response.
- Results grouped by session, high-importance first.
- Empty results trigger alternative-term suggestions, not invention.
- No session id or score was paraphrased or rounded.
See also
- `remember`: the write side; recall retrieves what it stores.
- `recap`, `handoff`, `session-history`: session-scoped views of the same data.
Troubleshooting
See ../_shared/TROUBLESHOOTING.md if `memory_smart_search` is not available.
Read more
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
Quick start
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)
Why
Only surface what the tool returned. Never fabricate an observation, a session id, or an importance score. If nothing comes back, say so.
Workflow
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. 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.
Anti-patterns
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`."
Checklist
- Every observation shown came from the tool response.
- Results grouped by session, high-importance first.
- Empty results trigger alternative-term suggestions, not invention.
- No session id or score was paraphrased or rounded.
See also
- `remember`: the write side; recall retrieves what it stores.
- `recap`, `handoff`, `session-history`: session-scoped views of the same data.
Troubleshooting
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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