Search your personal knowledge base to surface relevant past insights, decisions, and solutions.
Works with
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
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionsearch-memoryExecute the skills CLI command in your project's root directory to begin installation:
Fetches search-memory from nowledge-co/community and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate search-memory. Access via /search-memory in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Run in your terminal
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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
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
Found: Synthesize, cite when helpful None: State clearly, suggest distilling if current discussion valuable
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.
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
kostja94/marketing-skills
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
Keeps context tight: search-memory is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for search-memory matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend search-memory for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
I recommend search-memory for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
search-memory reduced setup friction for our internal harness; good balance of opinion and flexibility.
Keeps context tight: search-memory is the kind of skill you can hand to a new teammate without a long onboarding doc.
I recommend search-memory for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Registry listing for search-memory matched our evaluation — installs cleanly and behaves as described in the markdown.
search-memory reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend search-memory for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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