Productivity

search-memory

nowledge-co/community · updated Apr 8, 2026

$npx skills add https://github.com/nowledge-co/community --skill search-memory
summary

Search your personal knowledge base to surface relevant past insights, decisions, and solutions.

  • Proactively searches durable knowledge and conversation history when context suggests prior work would improve the response
  • Distinguishes between memory searches ( nmem m search ) for stored breakthroughs and thread searches ( nmem t search ) for exact session history
  • Recognizes trigger patterns: user references to prior fixes, resumed features, debugging similarities, requests for ration
skill.md

Search Memory

When to Search (Autonomous Recognition)

Strong signals:

  • Continuity: Current topic connects to prior work
  • Pattern match: Problem resembles past solved issue
  • Decision context: "Why/how we chose X" implies documented rationale
  • Recurring theme: Topic discussed in past sessions
  • Implicit recall: "that approach", "like before"

Contextual signals:

  • Complex debugging (may match past root causes)
  • Architecture discussion (choices may be documented)
  • Domain-specific question (conventions likely stored)

Skip when:

  • Fundamentally new topic
  • Generic syntax questions
  • Fresh perspective explicitly requested

Tool Usage

Use nmem CLI with --json flag for programmatic search:

# Basic search
nmem --json m search "3-7 core concepts"

# With filters
nmem --json m search "API design" --importance 0.8

# With labels (multiple labels use AND logic)
nmem --json m search "authentication" -l backend -l security

# With time filter
nmem --json m search "meeting notes" -t week

Query: Extract semantic core, preserve terminology, multi-language aware

Filters:

  • --importance MIN: Minimum importance score (0.0-1.0)
  • -l, --label LABEL: Filter by label (can specify multiple)
  • -t, --time RANGE: Time filter (today, week, month, year)
  • -n NUM: Limit number of results (default: 10)

JSON Response: Parse memories array, check score field for relevance

Use thread search when the user is really asking about a prior conversation, previous session, or exact discussion:

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

If a memory result includes source_thread or thread search finds the likely conversation, inspect it progressively instead of loading the whole thread at once:

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

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

Scores: 0.6-1.0 direct | 0.3-0.6 related | <0.3 skip

Examples:

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

# Search with multiple labels
nmem --json m search "React patterns" -l frontend -l react

# Search recent memories
nmem --json m search "bug fix" -t week -n 5

Response

Found: Synthesize, cite when helpful None: State clearly, suggest distilling if current discussion valuable

Troubleshooting

If nmem is not in PATH: pip install nmem-cli

For remote servers: create ~/.nowledge-mem/config.json with {"apiUrl": "...", "apiKey": "..."}.

Run /status to check server connection.