Search Memory

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