search-memory

nowledge-co/community · updated Apr 8, 2026

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

how to use search-memory

How to use search-memory on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add search-memory
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

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

The skills CLI fetches search-memory from GitHub repository nowledge-co/community and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/search-memory

Reload or restart Cursor to activate search-memory. Access the skill through slash commands (e.g., /search-memory) or your agent's skill management interface.

Security & Verification Notice

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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

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Use Cases

User Story & Requirements Generation

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

Competitive Analysis

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

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

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

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ 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.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.642 reviews
  • Olivia Nasser· Dec 20, 2024

    Keeps context tight: search-memory is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Pratham Ware· Dec 16, 2024

    Registry listing for search-memory matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Kwame Liu· Dec 16, 2024

    I recommend search-memory for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Noah Jackson· Nov 11, 2024

    I recommend search-memory for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Sakshi Patil· Nov 7, 2024

    search-memory reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Carlos Garcia· Nov 7, 2024

    Keeps context tight: search-memory is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Chaitanya Patil· Oct 26, 2024

    I recommend search-memory for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Carlos Haddad· Oct 26, 2024

    Registry listing for search-memory matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Noah Desai· Oct 2, 2024

    search-memory reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Anaya Anderson· Sep 21, 2024

    I recommend search-memory for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

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