---
name: Search Memory
slug: search-memory
category: AI Engineering
description: Search Memory looks up relevant past decisions, fixes, and context in your personal knowledge base. Use it when earlier work, a prior pattern, or stored rationale would improve the current answer.
github: "https://github.com/nowledge-co/community/tree/main/nowledge-mem-agent-plugin/skills/search-memory"
language: Python
stars: 166
forks: 35
install: "npx degit https://github.com/nowledge-co/community/tree/main/nowledge-mem-agent-plugin/skills/search-memory ~/.claude/skills/search-memory"
installs_to: ~/.claude/skills/search-memory
source_path: nowledge-mem-agent-plugin/skills/search-memory/SKILL.md
collection_size: 25
category_size: 3670
collection_url: "https://dirskills.com/collections/nowledge-co/community"
added: 2026-09-08T05:34:49.662Z
last_synced: 2026-09-08T05:34:49.662Z
canonical_url: "https://dirskills.com/skills/search-memory"
---

# Search Memory

Search Memory looks up relevant past decisions, fixes, and context in your personal knowledge base. Use it when earlier work, a prior pattern, or stored rationale would improve the current answer.

**Install:**

```bash
npx degit https://github.com/nowledge-co/community/tree/main/nowledge-mem-agent-plugin/skills/search-memory ~/.claude/skills/search-memory
```

## README

# Search Memory

> AI-powered search across your personal knowledge base using Nowledge Mem.

## 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
- 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 the user has established before
- the current result is ambiguous and past context would make the answer sharper

## Retrieval Routing

1. Start with `nmem --json m search` for durable knowledge.
2. Use `nmem --json t search` when the user is really asking about a prior conversation or exact session history.
3. If a result includes `source_thread`, inspect it progressively with `nmem --json t show <thread_id> --limit 8 --offset 0 --content-limit 1200`.
4. Prefer the smallest retrieval surface that answers the question.

For continuation-heavy engineering work, search near the start of the task. Do not wait for the user to literally ask for memory search.

If the host already knows the active project or agent lane, add `--space "<space name>"` to these commands.

## Native Connector

These skills work in any agent via CLI. For auto-recall, auto-capture, and graph tools, check if your agent has a native Nowledge Mem connector — run the `check-integration` skill or see https://mem.nowledge.co/docs/integrations