Search memory store when past insights would improve response. Recognize when user's stored breakthroughs, decisions, or solutions are relevant. Search proactively based on context, not just explicit requests.
/plugin marketplace add nowledge-co/community/plugin install nowledge-mem@nowledge-communityThis skill inherits all available tools. When active, it can use any tool Claude has access to.
Strong signals:
Contextual signals:
Skip when:
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
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
Found: Synthesize, cite when helpful None: State clearly, suggest distilling if current discussion valuable
If nmem is not available:
Option 1 (Recommended): Use uvx
# Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh
# Run nmem (no installation needed)
uvx nmem --version
Option 2: Install with pip
pip install nmem
nmem --version