Store a learning, pattern, or decision in the memory system for future recall.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionrememberExecute the skills CLI command in your project's root directory to begin installation:
Fetches remember from parcadei/continuous-claude-v3 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 remember. Access via /remember 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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Store a learning, pattern, or decision in the memory system for future recall.
/remember <what you learned>
Or with explicit type:
/remember --type WORKING_SOLUTION <what you learned>
/remember TypeScript hooks require npm install before they work
/remember --type ARCHITECTURAL_DECISION Session affinity uses terminal PID
/remember --type FAILED_APPROACH Don't use subshell for store_learning command
| Type | Use For |
|---|---|
WORKING_SOLUTION |
Fixes, solutions that worked (default) |
ARCHITECTURAL_DECISION |
Design choices, system structure |
CODEBASE_PATTERN |
Patterns discovered in code |
FAILED_APPROACH |
What didn't work |
ERROR_FIX |
Specific error resolutions |
When this skill is invoked, run:
cd $CLAUDE_OPC_DIR && PYTHONPATH=. uv run python scripts/core/store_learning.py \
--session-id "manual-$(date +%Y%m%d-%H%M)" \
--type <TYPE or WORKING_SOLUTION> \
--content "<ARGS>" \
--context "manual entry via /remember" \
--confidence medium
If no --type specified, infer from content:
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.
parcadei/continuous-claude-v3
mattpocock/skills
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
Registry listing for remember matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in remember — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
remember has been reliable in day-to-day use. Documentation quality is above average for community skills.
I recommend remember for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
I recommend remember for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
remember reduced setup friction for our internal harness; good balance of opinion and flexibility.
Keeps context tight: remember is the kind of skill you can hand to a new teammate without a long onboarding doc.
remember fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
remember has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: remember is focused, and the summary matches what you get after install.
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