---
name: Search Memory
slug: search-memory-3
category: AI Engineering
description: Search Memory finds prior decisions, procedures, and conversation history across Nowledge memories and threads. Use it when a task continues earlier work, needs rationale, or depends on exact past context.
github: "https://github.com/nowledge-co/community/tree/main/nowledge-mem-codex-plugin/skills/search-memory"
language: Python
stars: 166
forks: 35
install: "npx degit https://github.com/nowledge-co/community/tree/main/nowledge-mem-codex-plugin/skills/search-memory ~/.claude/skills/search-memory"
installs_to: ~/.claude/skills/search-memory
source_path: nowledge-mem-codex-plugin/skills/search-memory/SKILL.md
collection_size: 25
category_size: 3670
collection_url: "https://dirskills.com/collections/nowledge-co/community"
added: 2026-09-08T05:34:53.753Z
last_synced: 2026-09-08T05:34:53.753Z
canonical_url: "https://dirskills.com/skills/search-memory-3"
---

# Search Memory

Search Memory finds prior decisions, procedures, and conversation history across Nowledge memories and threads. Use it when a task continues earlier work, needs rationale, or depends on exact past context.

**Install:**

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

## README

Find what the user already knows. Search their memories and past conversations for decisions, procedures, and context that make the current task sharper.

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

Codex local Memory is useful as a hint, but it is not a substitute for this search when provenance, exact history, current cross-tool state, or prior decisions matter.

## 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

**When NOT to search:**

- Fundamentally new topic with no prior history
- Generic syntax or API questions answerable from documentation
- User explicitly asks for a fresh perspective without prior context

## Retrieval routing

If this session already exposes the Nowledge Mem MCP server, prefer:

1. `memory_search` for durable knowledge (decisions, insights, procedures).
2. `thread_search` when the user is asking about a prior conversation or exact session history.
3. `thread_fetch_messages` for progressive inspection of the matching thread.

Otherwise:

1. Start with `nmem --json m search "query"` for durable knowledge (decisions, insights, procedures).
2. Use `nmem --json t search "query" --limit 5` when the user is 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`.

Prefer the smallest retrieval that answers the question. Do not over-fetch.

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

## Deep mode

If results are weak or the need is conceptual/historical, try deeper matching:

```bash
nmem --json m search "query" --mode deep
```

## Knowledge tree routing

When the user needs to browse across multiple object types, inspect nearby context, or asks for a file/tree/vault-like view, use the Knowledge Filesystem instead of only flat search.

Prefer MCP `mem_fs` when available:

```text
capabilities
recall "session token strategy" --in /memories -k 5
find /memories --label decisions --since 2026-01-01
grep "JWT rotation" /memories
grep -E "JWT|token" /threads
cat /memories/by-id/<id>.memory.md
```

Otherwise use:

```bash
nmem fs capabilities --json
nmem fs recall "session token strategy" --in /memories -k 5
nmem fs ls /wiki
nmem fs cat /wiki/topics/<topic>.topic.md
```

Use `capabilities` before assuming roots or future verbs. Use `recall` for fuzzy phrasing, `find` for metadata constraints, `grep` for exact strings, `grep -E` for explicit regex, then `stat` or `cat` the returned paths. KFS paths are Mem identifiers, not local OS files; mount and SQL/Cypher are later phases.

## Filters

Add filters only when the task clearly implies them:

- By label: `-l "label-name"`
- By importance: `--importance 0.7`
- By date range: `--event-from 2026-01-01` / `--event-to 2026-03-01`
- By source: `-s codex`
- Limit results: `-n 10`

Summarize only the strongest matches and clearly say when nothing relevant was found.

## Links

- [Search](https://mem.nowledge.co/docs/search-relevance)