Run autonomous research tasks that plan, search, read, and synthesize information into comprehensive reports.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiondeep-researchExecute the skills CLI command in your project's root directory to begin installation:
Fetches deep-research from sanjay3290/ai-skills 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 deep-research. Access via /deep-research 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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Run autonomous research tasks that plan, search, read, and synthesize information into comprehensive reports.
pip install -r requirements.txtexport GEMINI_API_KEY=your-api-key-here
.env file in the skill directory.python3 scripts/research.py --query "Research the history of Kubernetes"
python3 scripts/research.py --query "Compare Python web frameworks" \
--format "1. Executive Summary\n2. Comparison Table\n3. Recommendations"
python3 scripts/research.py --query "Analyze EV battery market" --stream
python3 scripts/research.py --query "Research topic" --no-wait
python3 scripts/research.py --status <interaction_id>
python3 scripts/research.py --wait <interaction_id>
python3 scripts/research.py --query "Elaborate on point 2" --continue <interaction_id>
python3 scripts/research.py --list
--json): Structured data for programmatic use--raw): Unprocessed API response| Metric | Value |
|---|---|
| Time | 2-10 minutes per task |
| Cost | $2-5 per task (varies by complexity) |
| Token usage | ~250k-900k input, ~60k-80k output |
--query "..."--stream or poll with --status--continue for follow-up questionsMake 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
I recommend deep-research for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: deep-research is the kind of skill you can hand to a new teammate without a long onboarding doc.
deep-research reduced setup friction for our internal harness; good balance of opinion and flexibility.
Keeps context tight: deep-research is the kind of skill you can hand to a new teammate without a long onboarding doc.
I recommend deep-research for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Registry listing for deep-research matched our evaluation — installs cleanly and behaves as described in the markdown.
Registry listing for deep-research matched our evaluation — installs cleanly and behaves as described in the markdown.
deep-research reduced setup friction for our internal harness; good balance of opinion and flexibility.
Keeps context tight: deep-research is the kind of skill you can hand to a new teammate without a long onboarding doc.
I recommend deep-research for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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