Search Memory

nowledge-co/community/nowledge-mem-openhands-plugin/skills/search-memory

by nowledge-co1f4d7eb61466dbfc93ce8d7a0a806c4d4a196dd0No licenseListed Oct 9, 2026Updated Oct 9, 2026

Search your personal knowledge base when past insights would improve response. Recognize when stored breakthroughs, decisions, or solutions are relevant. Search proactively based on context, not just explicit requests.

Instructions onlyProductivity & Workflow
AI-generated overview

Guides proactive searching of a personal knowledge base for past insights, decisions, and solutions.

What it does
This skill provides instructions for searching a personal knowledge base, called Nowledge Mem, when earlier insights, decisions, or solutions would improve a response. It describes when to search, how to query memories, threads, and the knowledge graph through MCP tools or the nmem CLI, and how to follow graph links and avoid repeating empty searches. It produces search queries and retrieved memory context rather than files or code.
When to use it
Use it when a task references previous work, a prior fix, or an earlier decision, or resumes a named feature, bug, refactor, incident, or subsystem. It also fits reviews, regressions, release or docs-alignment questions, debugging that resembles an earlier fix, and implicit recall language such as "that approach" or "like before".
Requirements
Requires access to Nowledge Mem, either through MCP tools (memory_search, thread_search, explore_graph) or the nmem CLI, plus any needed space scoping. It ships no scripts; it is instructions only.

Search Memory

Proactive search across your personal knowledge base using Nowledge Mem in OpenHands.

When to Use

Strong signals — search when:

  • the user references previous work, a prior fix, or an earlier decision
  • the task resumes a named feature, bug, refactor, incident, or subsystem
  • the task is a review, regression, release, docs-alignment, or connector-behavior question
  • an OpenHands specialist node needs architecture guidelines agreed on by previous nodes
  • a debugging pattern resembles something solved earlier
  • the user asks for rationale, preferences, procedures, or recurring workflow details
  • the user uses implicit recall language: "that approach", "like before", "the pattern we used"

Contextual signals — consider searching when:

  • complex debugging where prior context would narrow the search space
  • architecture discussion that may intersect with past decisions
  • domain-specific conventions established previously

Usage

Via MCP (Preferred)

Search memories:

json
memory_search({  "query": "PostgreSQL transaction isolation levels"})

Search prior conversation threads:

json
thread_search({  "query": "database connection pool leak fix"})

Explore knowledge graph around an entity:

json
explore_graph({  "query": "authentication service"})

Via CLI

Search memories:

bash
nmem --json m search "authentication service"

Search threads:

bash
nmem --json t search "authentication service"

If scoped to a specific space:

bash
nmem --json m search "authentication service" --space "Backend"

Search Strategy

  1. Be specific: query with technical terms, error messages, or architectural names.
  2. Follow graph links: when a memory contains deepLinks or entity relations, inspect related nodes if more context is needed.
  3. Do not repeat: if a recent search returned empty results, do not re-run identical searches within the same turn.

Source and attribution

Source:nowledge-co/communityinnowledge-mem-openhands-plugin/skills/search-memoryat commit1f4d7eb

License: No license

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