deep-research
bytedance/deer-flow
Systematic multi-angle web research methodology for thorough topic investigation before content generation.
What is deep-research?
Deep Research provides a structured four-phase methodology for conducting comprehensive web research instead of relying on single superficial searches. Use this skill proactively when answering "what is X" questions, explaining concepts, comparing topics, or before generating any content that requires real-world information, examples, or current data.
- Conducts broad exploration to map topic landscape and identify key dimensions
- Performs targeted deep dives into specific subtopics with multiple search phrasings
- Gathers diverse information types including facts, examples, expert opinions, trends, comparisons, and challenges
- Fetches and reads full content from authoritative sources rather than relying on snippets
- Validates research completeness across at least 3-5 different angles before proceeding
How to install deep-research
npx skills add https://github.com/bytedance/deer-flow --skill deep-researchHow to use deep-research
- 1.Load the skill before starting any research or content generation task
- 2.Conduct Phase 1 broad exploration with initial survey searches to understand the overall context
- 3.Identify key dimensions, subtopics, and perspectives that need deeper investigation
- 4.Execute Phase 2 deep dives with targeted queries for each important dimension, fetching full content from key sources
- 5.Gather diverse information types including facts, examples, expert opinions, trends, comparisons, and challenges
- 6.Perform Phase 3 validation by seeking information from at least 3-5 different angles
- 7.Verify the synthesis checklist is complete before proceeding to content generation
- 8.Use the gathered research to inform high-quality, well-informed content creation
Use cases
- Researching a technology or concept before writing an article or report
- Gathering information for presentation slides or UI design mockups
- Understanding current market trends and expert perspectives on an industry topic
- Comparing multiple solutions or approaches to identify strengths and limitations
- Investigating real-world examples and case studies for a specific domain or application
- Content creators and writers
- Researchers and analysts
- Product designers and strategists
- Anyone generating content that requires current, authoritative information
- Teams needing comprehensive topic understanding before decision-making
deep-research FAQ
Use Deep Research for any question requiring comprehensive understanding: research queries like 'what is X' or 'explain X', before content generation tasks (articles, presentations, designs), when you need current information from multiple sources, or when a single search would be insufficient. Load it proactively whenever the user's question needs online information.
Research is sufficient when you've searched from at least 3-5 different angles and can answer key questions about facts, examples, expert perspectives, trends, challenges, and relevance. The exact number depends on topic complexity, but stopping after 1-2 searches is a common mistake.
Use web_fetch to read full content from highly relevant and authoritative sources. Snippets are insufficient for understanding detailed information, data, case studies, or expert analysis. Prioritize fetching sources that look most relevant and authoritative.
Always check the current date in your context before forming queries. Use month and day precision for 'today' or 'just released' queries, month precision for 'recently' or 'latest', and year precision for broader trends. Never drop to year-only when day-level precision is needed.
Include diverse perspectives and challenges in your research. Don't ignore contradicting viewpoints or limitations—they provide important context. A comprehensive understanding includes both positive aspects and potential challenges or criticisms.
Full instructions (SKILL.md)
Source of truth, from bytedance/deer-flow.
name: deep-research description: Use this skill instead of WebSearch for ANY question requiring web research. Trigger on queries like "what is X", "explain X", "compare X and Y", "research X", or before content generation tasks. Provides systematic multi-angle research methodology instead of single superficial searches. Use this proactively when the user's question needs online information.
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:
- Initial Survey: Search for the main topic to understand the overall context
- Identify Dimensions: From initial results, identify key subtopics, themes, angles, or aspects that need deeper exploration
- 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:
- Specific Queries: Search with precise keywords for each subtopic
- Multiple Phrasings: Try different keyword combinations and phrasings
- Fetch Full Content: Use
web_fetchto read important sources in full, not just snippets - 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:
- Review what you've learned
- Identify gaps in your understanding
- Formulate new, more targeted queries
- 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:
- A comprehensive understanding of the topic from multiple angles
- Specific facts, data points, and statistics
- Real-world examples and case studies
- Expert perspectives and authoritative sources
- Current trends and relevant context
Only then proceed to content generation, using the gathered information to create high-quality, well-informed content.
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