Agent skill

search-memory

Search your knowledge base when past decisions, preferences, or procedures would improve the response. Covers memories from every AI tool you use.

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Install this agent skill to your Project

npx add-skill https://github.com/nowledge-co/community/tree/main/nowledge-mem-pi-package/skills/search-memory

SKILL.md

Search Memory

When to Search

Strong signals:

  • References prior work: "the approach we used", "like last time"
  • Resumes a named feature, project, or migration
  • Review, regression, release, docs-alignment, or integration-behavior task
  • Debugging resembles a past fix or known root cause
  • Asks for rationale: "why did we choose X?"
  • Recurring theme discussed in earlier sessions

Contextual signals:

  • Complex debugging (may match past root causes)
  • Architecture discussion (choices may be documented)
  • Domain-specific question (conventions likely stored)
  • User mentions a timeframe: "last week", "back in January"

Skip when:

  • Fundamentally new topic with no prior context
  • Generic syntax or language questions
  • User explicitly requests a fresh perspective

Retrieval Routing

1. Search memories (distilled knowledge)

bash
nmem --json m search "3-7 word semantic query"

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

2. Search threads (past conversations)

When the user asks about a prior session, discussion, or exact exchange:

bash
nmem --json t search "query" --limit 5

3. Progressive thread inspection

If a thread looks relevant, load it incrementally:

bash
nmem --json t show <thread_id> --limit 8 --offset 0 --content-limit 1200

Increase --offset only when more messages are actually needed.

For continuation-heavy engineering work, search near the start of the task rather than waiting for an explicit recall request.

Key Flags

Flag Purpose
--mode deep Conceptual or weak first-pass results
-l label Filter by label (multiple uses AND logic)
-n limit Limit number of results (default: 10)
--importance MIN Minimum importance score (0.0-1.0)
--time RANGE Time filter: today, week, month, year

Examples

bash
# Semantic search with importance filter
nmem --json m search "database optimization" --importance 0.7

# Filter by labels
nmem --json m search "React patterns" -l frontend -l react

# Recent memories only
nmem --json m search "deployment fix" --time week -n 5

# Deep mode for conceptual queries
nmem --json m search "auth architecture rationale" --mode deep

Interpreting Results

Scores: 0.6-1.0 direct match. 0.3-0.6 related. Below 0.3, skip.

Found: Synthesize and cite when helpful. None: State clearly. Suggest distilling if the current discussion is valuable.

When NOT to Search

Do not search for every message. Search when there is a reasonable expectation that prior knowledge exists and would improve the response. One well-targeted search is better than three speculative ones.

Links

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