Recall

by rohitg00007a1a7fe864No license29K starsListed Oct 8, 2026Updated Oct 8, 2026Repository updated today

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.

Instructions onlyAI & Agents
AI-generated overview

Retrieves past agent-memory observations and sessions on a topic using hybrid search, reporting only what the tool returns.

What it does
Runs a memory_smart_search query against an agent-memory store and presents matching past observations, grouped by session and ordered by importance. Each result shows its type, title, and narrative, with provenance channels used to resolve conflicts. If nothing matches, it suggests alternative search terms instead of inventing results.
When to use it
Use when the user asks to recall, or asks what was done about, whether something was ever seen, or needs context from earlier sessions. Also useful when scoping a search to a specific project or repository.
Requirements
Requires access to an agent-memory search tool named memory_smart_search; no scripts are shipped, only instructions. A shared troubleshooting document is referenced for when that tool is unavailable.

The user wants to recall past context about: $ARGUMENTS

Quick start

json
memory_smart_search { "query": "jwt refresh token rotation", "limit": 10 }

Expected output:

text
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. 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.

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.
  • memory-discipline: when to run this search unprompted.

Troubleshooting

See ../_shared/TROUBLESHOOTING.md if memory_smart_search is not available.

Source and attribution

Source:rohitg00/agentmemoryinplugin/skills/recallat commit007a1a7

License: No license

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