super-search
Search your coding memory for past work, sessions, and implementation details.
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What it does
Queries personal session memories, project/repo memories, or both in parallel using optional scope flags ( --user , --repo , --both )
Returns formatted results with timestamps and relevance scores to help recall previous decisions, implementations, and coding patterns
Designed for retrieving context about past work when users ask about earlier sessions, how something was implemented, or what they worked on
Installation Guide
How to use super-search on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your machine
- ›Node.js 16+ with npm — verify with
node --version - ›Active project directory where you want to add
super-search
Run the install command
Execute the skills CLI command in your project's root directory to begin installation:
Fetches super-search from supermemoryai/claude-supermemory and configures it for Cursor.
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate super-search. Access via /super-search in your agent's command palette.
Security 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 environment. Always review source, verify the publisher, and test in isolation before production.
Documentation
Super Search
Search Supermemory for past coding sessions, decisions, and saved information.
How to Search
Run the search script with the user's query and optional scope flag:
node "${CLAUDE_PLUGIN_ROOT}/scripts/search-memory.cjs" [--user|--repo|--both] "USER_QUERY_HERE"
Scope Flags
--both(default): Search both personal session and project memories across team members in parallel--user: Search personal/user memories across sessions--repo: Search project/repo memories across team members
Examples
-
User asks "what did I work on yesterday":
node "${CLAUDE_PLUGIN_ROOT}/scripts/search-memory.cjs" "work yesterday recent activity" -
User asks "how did we implement auth" (project-specific):
node "${CLAUDE_PLUGIN_ROOT}/scripts/search-memory.cjs" --repo "authentication implementation" -
User asks "what are my coding preferences":
node "${CLAUDE_PLUGIN_ROOT}/scripts/search-memory.cjs" --user "coding preferences style"
Present Results
The script outputs formatted memory results with timestamps and relevance scores. Present them clearly to the user and offer to search again with different terms if needed.
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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
Steps
- 1Install product management skill
- 2Start with user story generation for known feature
- 3Progress to competitive analysis: research 2-3 competitors
- 4Use for roadmap prioritization: apply RICE/ICE scoring
- 5Draft stakeholder communications and refine based on feedback
- 6Build template library for recurring PM tasks
- 7Share 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
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
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Reviews
- AAmelia Zhang★★★★★Dec 12, 2024
super-search fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- MMeera Shah★★★★★Dec 12, 2024
super-search is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- DDev Gupta★★★★★Dec 4, 2024
super-search has been reliable in day-to-day use. Documentation quality is above average for community skills.
- LLayla Sethi★★★★★Dec 4, 2024
We added super-search from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- TTariq Taylor★★★★★Dec 4, 2024
Useful defaults in super-search — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- LLayla Reddy★★★★★Nov 23, 2024
super-search fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- HHassan Gill★★★★★Nov 23, 2024
Solid pick for teams standardizing on skills: super-search is focused, and the summary matches what you get after install.
- MMeera Sharma★★★★★Nov 11, 2024
I recommend super-search for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- TTariq Johnson★★★★★Nov 3, 2024
super-search has been reliable in day-to-day use. Documentation quality is above average for community skills.
- AAisha Jain★★★★★Nov 3, 2024
super-search reduced setup friction for our internal harness; good balance of opinion and flexibility.
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