deep-research

bytedance/deer-flow · updated Apr 8, 2026

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$npx skills add https://github.com/bytedance/deer-flow --skill deep-research
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summary

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.

skill.md

Deep Research Skill

Overview

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.

When to Use This Skill

Always load this skill when:

Research Questions

  • User asks "what is X", "explain X", "research X", "investigate X"
  • User wants to understand a concept, technology, or topic in depth
  • The question requires current, comprehensive information from multiple sources
  • A single web search would be insufficient to answer properly

Content Generation (Pre-research)

  • Creating presentations (PPT/slides)
  • Creating frontend designs or UI mockups
  • Writing articles, reports, or documentation
  • Producing videos or multimedia content
  • Any content that requires real-world information, examples, or current data

Core Principle

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.

Research Methodology

Phase 1: Broad Exploration

Start with broad searches to understand the landscape:

  1. Initial Survey: Search for the main topic to understand the overall context
  2. Identify Dimensions: From initial results, identify key subtopics, themes, angles, or aspects that need deeper exploration
  3. Map the Territory: Note different perspectives, stakeholders, or viewpoints that exist

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

Phase 2: Deep Dive

For each important dimension identified, conduct targeted research:

  1. Specific Queries: Search with precise keywords for each subtopic
  2. Multiple Phrasings: Try different keyword combinations and phrasings
  3. Fetch Full Content: Use web_fetch to read important sources in full, not just snippets
  4. Follow References: When sources mention other important resources, search for those too

Example:

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

Phase 3: Diversity & Validation

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"

Phase 4: Synthesis Check

Before proceeding to content generation, verify:

  • Have I searched from at least 3-5 different angles?
  • Have I fetched and read the most important sources in full?
  • Do I have concrete data, examples, and expert perspectives?
  • Have I explored both positive aspects and challenges/limitations?
  • Is my information current and from authoritative sources?

If any answer is NO, continue researching before generating content.

Search Strategy Tips

Effective Query Patterns

# 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"

Temporal Awareness

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:

  • When the user asks about "today" or "just released", use month + day + year in your search queries to get same-day results
  • Never drop to year-only when day-level precision is needed — "tech news 2026" will NOT surface today's news
  • Try multiple phrasings: numeric form (2026-02-28), 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

When to Use web_fetch

Use web_fetch to read full content when:

  • A search result looks highly relevant and authoritative
  • You need detailed information beyond the snippet
  • The source contains data, case studies, or expert analysis
  • You want to understand the full context of a finding

Iterative Refinement

Research is iterative. After initial searches:

  1. Review what you've learned
  2. Identify gaps in your understanding
  3. Formulate new, more targeted queries
  4. Repeat until you have comprehensive coverage

Quality Bar

Your research is sufficient when you can confidently answer:

  • What are the key facts and data points?
  • What are 2-3 concrete real-world examples?
  • What do experts say about this topic?
  • What are the current trends and future directions?
  • What are the challenges or limitations?
  • What makes this topic relevant or important now?

Common Mistakes to Avoid

  • ❌ Stopping after 1-2 searches
  • ❌ Relying on search snippets without reading full sources
  • ❌ Searching only one aspect of a multi-faceted topic
  • ❌ Ignoring contradicting viewpoints or challenges
  • ❌ Using outdated information when current data exists
  • ❌ Starting content generation before research is complete

Output

After completing research, you should have:

  1. A comprehensive understanding of the topic from multiple angles
  2. Specific facts, data points, and statistics
  3. Real-world examples and case studies
  4. Expert perspectives and authoritative sources
  5. Current trends and relevant context

Only then proceed to content generation, using the gathered information to create high-quality, well-informed content.

how to use deep-research

How to use deep-research on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add deep-research
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/bytedance/deer-flow --skill deep-research

The skills CLI fetches deep-research from GitHub repository bytedance/deer-flow and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/deep-research

Reload or restart Cursor to activate deep-research. Access the skill through slash commands (e.g., /deep-research) or your agent's skill management interface.

Security & Verification 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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

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

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share 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

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.733 reviews
  • Anika Sethi· Dec 28, 2024

    We added deep-research from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Ganesh Mohane· Dec 20, 2024

    I recommend deep-research for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Shikha Mishra· Dec 16, 2024

    deep-research is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • William Garcia· Nov 27, 2024

    deep-research reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Mia Harris· Nov 19, 2024

    Useful defaults in deep-research — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Yash Thakker· Nov 7, 2024

    Keeps context tight: deep-research is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Dhruvi Jain· Oct 26, 2024

    Registry listing for deep-research matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Ava Robinson· Oct 18, 2024

    I recommend deep-research for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Anaya Mensah· Oct 10, 2024

    deep-research has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Ava Gonzalez· Sep 1, 2024

    We added deep-research from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

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