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mem-search▌

thedotmack/claude-mem · updated Apr 8, 2026

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$npx skills add https://github.com/thedotmack/claude-mem --skill mem-search
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summary

Search persistent cross-session memory to answer questions about previous work and past solutions.

  • ›Three-layer workflow: search for IDs and metadata (50–100 tokens per result), optionally view timeline context around interesting results, then batch-fetch full details only for filtered IDs
  • ›Search parameters include query text, project name, observation type (bugfix, feature, decision, discovery, change), and date range filtering
  • ›Timeline tool shows observations, sessions, and prompts
skill.md

Memory Search

Search past work across all sessions. Simple workflow: search -> filter -> fetch.

When to Use

Use when users ask about PREVIOUS sessions (not current conversation):

  • "Did we already fix this?"
  • "How did we solve X last time?"
  • "What happened last week?"

3-Layer Workflow (ALWAYS Follow)

NEVER fetch full details without filtering first. 10x token savings.

Step 1: Search - Get Index with IDs

Use the search MCP tool:

search(query="authentication", limit=20, project="my-project")

Returns: Table with IDs, timestamps, types, titles (~50-100 tokens/result)

| ID | Time | T | Title | Read |
|----|------|---|-------|------|
| #11131 | 3:48 PM | 🟣 | Added JWT authentication | ~75 |
| #10942 | 2:15 PM | 🔴 | Fixed auth token expiration | ~50 |

Parameters:

  • query (string) - Search term
  • limit (number) - Max results, default 20, max 100
  • project (string) - Project name filter
  • type (string, optional) - "observations", "sessions", or "prompts"
  • obs_type (string, optional) - Comma-separated: bugfix, feature, decision, discovery, change
  • dateStart (string, optional) - YYYY-MM-DD or epoch ms
  • dateEnd (string, optional) - YYYY-MM-DD or epoch ms
  • offset (number, optional) - Skip N results
  • orderBy (string, optional) - "date_desc" (default), "date_asc", "relevance"

Step 2: Timeline - Get Context Around Interesting Results

Use the timeline MCP tool:

timeline(anchor=11131, depth_before=3, depth_after=3, project="my-project")

Or find anchor automatically from query:

timeline(query="authentication", depth_before=3, depth_after=3, project="my-project")

Returns: depth_before + 1 + depth_after items in chronological order with observations, sessions, and prompts interleaved around the anchor.

Parameters:

  • anchor (number, optional) - Observation ID to center around
  • query (string, optional) - Find anchor automatically if anchor not provided
  • depth_before (number, optional) - Items before anchor, default 5, max 20
  • depth_after (number, optional) - Items after anchor, default 5, max 20
  • project (string) - Project name filter

Step 3: Fetch - Get Full Details ONLY for Filtered IDs

Review titles from Step 1 and context from Step 2. Pick relevant IDs. Discard the rest.

Use the get_observations MCP tool:

get_observations(ids=[11131, 10942])

ALWAYS use get_observations for 2+ observations - single request vs N requests.

Parameters:

  • ids (array of numbers, required) - Observation IDs to fetch
  • orderBy (string, optional) - "date_desc" (default), "date_asc"
  • limit (number, optional) - Max observations to return
  • project (string, optional) - Project name filter

Returns: Complete observation objects with title, subtitle, narrative, facts, concepts, files (~500-1000 tokens each)

Examples

Find recent bug fixes:

search(query="bug", type="observations", obs_type="bugfix", limit=20, project="my-project")

Find what happened last week:

search(type="observations", dateStart="2025-11-11", limit=20, project="my-project")

Understand context around a discovery:

timeline(anchor=11131, depth_before=5, depth_after=5, project="my-project")

Batch fetch details:

get_observations(ids=[11131, 10942, 10855], orderBy="date_desc")

Why This Workflow?

  • Search index: ~50-100 tokens per result
  • Full observation: ~500-1000 tokens each
  • Batch fetch: 1 HTTP request vs N individual requests
  • 10x token savings by filtering before fetching
how to use mem-search

How to use mem-search 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 mem-search
2

Execute installation command

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

$npx skills add https://github.com/thedotmack/claude-mem --skill mem-search

The skills CLI fetches mem-search from GitHub repository thedotmack/claude-mem 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/mem-search

Reload or restart Cursor to activate mem-search. Access the skill through slash commands (e.g., /mem-search) 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.

Additional Resources

›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

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

4.4★★★★★56 reviews
  • ★★★★★Li Malhotra· Dec 28, 2024

    mem-search has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • ★★★★★Noor Kapoor· Dec 24, 2024

    mem-search fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • ★★★★★Ishan Ndlovu· Dec 4, 2024

    Useful defaults in mem-search — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • ★★★★★Rahul Santra· Nov 23, 2024

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

  • ★★★★★Carlos Kim· Nov 23, 2024

    mem-search is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • ★★★★★Mei Sharma· Nov 19, 2024

    Solid pick for teams standardizing on skills: mem-search is focused, and the summary matches what you get after install.

  • ★★★★★Carlos Huang· Nov 19, 2024

    We added mem-search from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • ★★★★★Ira Agarwal· Nov 15, 2024

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

  • ★★★★★Camila Martin· Nov 3, 2024

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

  • ★★★★★Valentina Martin· Oct 22, 2024

    We added mem-search from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

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