Search persistent cross-session memory to answer questions about previous work and past solutions.
Works with
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
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionmem-searchExecute the skills CLI command in your project's root directory to begin installation:
Fetches mem-search from thedotmack/claude-mem 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 mem-search. Access via /mem-search 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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Search past work across all sessions. Simple workflow: search -> filter -> fetch.
Use when users ask about PREVIOUS sessions (not current conversation):
NEVER fetch full details without filtering first. 10x token savings.
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 termlimit (number) - Max results, default 20, max 100project (string) - Project name filtertype (string, optional) - "observations", "sessions", or "prompts"obs_type (string, optional) - Comma-separated: bugfix, feature, decision, discovery, changedateStart (string, optional) - YYYY-MM-DD or epoch msdateEnd (string, optional) - YYYY-MM-DD or epoch msoffset (number, optional) - Skip N resultsorderBy (string, optional) - "date_desc" (default), "date_asc", "relevance"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 aroundquery (string, optional) - Find anchor automatically if anchor not provideddepth_before (number, optional) - Items before anchor, default 5, max 20depth_after (number, optional) - Items after anchor, default 5, max 20project (string) - Project name filterReview 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 fetchorderBy (string, optional) - "date_desc" (default), "date_asc"limit (number, optional) - Max observations to returnproject (string, optional) - Project name filterReturns: Complete observation objects with title, subtitle, narrative, facts, concepts, files (~500-1000 tokens each)
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")
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
ailabs-393/ai-labs-claude-skills
mem-search has been reliable in day-to-day use. Documentation quality is above average for community skills.
mem-search fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Useful defaults in mem-search — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Keeps context tight: mem-search is the kind of skill you can hand to a new teammate without a long onboarding doc.
mem-search is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: mem-search is focused, and the summary matches what you get after install.
We added mem-search from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend mem-search for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: mem-search is the kind of skill you can hand to a new teammate without a long onboarding doc.
We added mem-search from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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