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rohitg00/agentmemory13k installs

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.

How do I install this agent skill?

npx skills add https://github.com/rohitg00/agentmemory --skill recall
view source ↗

Is this agent skill safe to install?

  • Gen Agent Trust Hubpass

    The skill provides a mechanism for searching and retrieving past agent observations and sessions from a memory tool. Analysis reveals no malicious patterns or security risks.

  • Socketpass

    No alerts

  • Snykpass

    Risk: LOW · No issues

  • ZeroLeakspass

    Score: 93/100 · 2 sections analyzed

What does this agent skill do?

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 and agentId when they request one agent's history. Use targetLayer: "memory" for explicitly saved knowledge, or targetLayer: "observation" for captured activity. Omit it for mixed search.
  2. Search results are compact. Before quoting full content or describing provenance, call memory_smart_search with the selected IDs as a comma-separated expandIds string and the same project, agent, and layer filters. If expansion fails or the record is no longer available, report that limitation; a title alone is not the full fact.
  3. Group expanded observations by session and show the returned type, title, and narrative. Include provenance only when the response supplies it.
  4. Lead with the high-signal observations (importance >= 7).
  5. With memory_recall, inspect matched_count, excluded_by_budget, and content_truncated before treating an empty or shortened response as absent knowledge. Recover full content with memory_smart_search using expandIds and the same project, agent, and layer filters. minimum_budget reports the estimated budget needed for a minimal preview when the budget is too small.
  6. If there are no matches, 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 TROUBLESHOOTING.md if memory_smart_search is not available.

Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.

<a href="https://skillzs.dev/skills/rohitg00/agentmemory/recall">View recall on skillZs</a>