A CLI tool for storing and retrieving memories with full-text search. Data is stored locally in ~/.mem/mem.db.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionmemExecute the skills CLI command in your project's root directory to begin installation:
Fetches mem from runablehq/memory 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. Access via /mem 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.
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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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A CLI tool for storing and retrieving memories with full-text search. Data is stored locally in ~/.mem/mem.db.
Three operators: (none) = recall, + = remember, - = forget.
mem # list recent memories
mem "deploy" # full-text search
mem "database" --tag db # search filtered by tag
mem 7sjtNVyZrNIa # get full content by ID
mem --tag prefs # list filtered by tag
mem "api" --limit 5 --json # limit results, JSON output
mem --full # show full content for all
mem + "user prefers dark mode" --tag prefs
mem + "deploy: bun build --compile" --tag deploy
mem + "chose SQLite for simplicity" --tag architecture
mem + --image ./screenshot.png --title "Current UI" --tag ui
echo "long content" | mem + --tag notes
mem - <id> # delete one memory
mem - id1 id2 id3 # delete multiple
mem "old" --json | jq -r '.[].id' | xargs -I{} mem - {}
echo "long content" | mem + --tag notes
prefs, api, deploy, db--full: Complete content inline--json: Structured JSON for parsingMake 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
Solid pick for teams standardizing on skills: mem is focused, and the summary matches what you get after install.
Keeps context tight: mem is the kind of skill you can hand to a new teammate without a long onboarding doc.
mem is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Registry listing for mem matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in mem — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
We added mem from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend mem for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
mem fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for mem matched our evaluation — installs cleanly and behaves as described in the markdown.
mem is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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