This skill provides a systematic methodology for conducting thorough web research. Load this skill BEFORE starting any content generation task to ensure you gather sufficient information from multiple angles, depths, and sources.
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 bytedance/deer-flow 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.
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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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This skill provides a systematic methodology for conducting thorough web research. Load this skill BEFORE starting any content generation task to ensure you gather sufficient information from multiple angles, depths, and sources.
Always load this skill when:
Never generate content based solely on general knowledge. The quality of your output directly depends on the quality and quantity of research conducted beforehand. A single search query is NEVER enough.
Start with broad searches to understand the landscape:
Example:
Topic: "AI in healthcare"
Initial searches:
- "AI healthcare applications 2024"
- "artificial intelligence medical diagnosis"
- "healthcare AI market trends"
Identified dimensions:
- Diagnostic AI (radiology, pathology)
- Treatment recommendation systems
- Administrative automation
- Patient monitoring
- Regulatory landscape
- Ethical considerations
For each important dimension identified, conduct targeted research:
web_fetch to read important sources in full, not just snippetsExample:
Dimension: "Diagnostic AI in radiology"
Targeted searches:
- "AI radiology FDA approved systems"
- "chest X-ray AI detection accuracy"
- "radiology AI clinical trials results"
Then fetch and read:
- Key research papers or summaries
- Industry reports
- Real-world case studies
Ensure comprehensive coverage by seeking diverse information types:
| Information Type | Purpose | Example Searches |
|---|---|---|
| Facts & Data | Concrete evidence | "statistics", "data", "numbers", "market size" |
| Examples & Cases | Real-world applications | "case study", "example", "implementation" |
| Expert Opinions | Authority perspectives | "expert analysis", "interview", "commentary" |
| Trends & Predictions | Future direction | "trends 2024", "forecast", "future of" |
| Comparisons | Context and alternatives | "vs", "comparison", "alternatives" |
| Challenges & Criticisms | Balanced view | "challenges", "limitations", "criticism" |
Before proceeding to content generation, verify:
If any answer is NO, continue researching before generating content.
# Be specific with context
❌ "AI trends"
✅ "enterprise AI adoption trends 2024"
# Include authoritative source hints
"[topic] research paper"
"[topic] McKinsey report"
"[topic] industry analysis"
# Search for specific content types
"[topic] case study"
"[topic] statistics"
"[topic] expert interview"
# Use temporal qualifiers — always use the ACTUAL current year from <current_date>
"[topic] 2026" # ← replace with real current year, never hardcode a past year
"[topic] latest"
"[topic] recent developments"
Always check <current_date> in your context before forming ANY search query.
<current_date> gives you the full date: year, month, day, and weekday (e.g. 2026-02-28, Saturday). Use the right level of precision depending on what the user is asking:
| User intent | Temporal precision needed | Example query |
|---|---|---|
| "today / this morning / just released" | Month + Day | "tech news February 28 2026" |
| "this week" | Week range | "technology releases week of Feb 24 2026" |
| "recently / latest / new" | Month | "AI breakthroughs February 2026" |
| "this year / trends" | Year | "software trends 2026" |
Rules:
"tech news 2026" will NOT surface today's news2026-02-28), written form (February 28 2026), and relative terms (today, this week) across different queries❌ User asks "what's new in tech today" → searching "new technology 2026" → misses today's news
✅ User asks "what's new in tech today" → searching "new technology February 28 2026" + "tech news today Feb 28" → gets today's results
Use web_fetch to read full content when:
Research is iterative. After initial searches:
Your research is sufficient when you can confidently answer:
After completing research, you should have:
Only then proceed to content generation, using the gathered information to create high-quality, well-informed 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
We added deep-research from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend deep-research for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
deep-research is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
deep-research reduced setup friction for our internal harness; good balance of opinion and flexibility.
Useful defaults in deep-research — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Keeps context tight: deep-research is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for deep-research matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend deep-research for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
deep-research has been reliable in day-to-day use. Documentation quality is above average for community skills.
We added deep-research from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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