deep-research

name	deep-research
description	Use this skill for deep research reports and systematic research across web sources, local LLM Wiki knowledge bases, or both. Trigger on queries like "what is X", "explain X", "compare X and Y", "research X", "generate a report", or wiki-grounded research requests. Provides multi-angle research and subagent orchestration methodology instead of single superficial searches.

Deep Research Skill

Overview

This skill provides a systematic methodology for conducting thorough research with DeerFlow. Load this skill BEFORE starting any research report or 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

Wiki-Grounded Research

• User asks to generate a report from an LLM Wiki or knowledge base
• User provides a wiki name/path and asks for a deep research report
• The task needs both current web evidence and existing local wiki knowledge
• The report should reuse curated local sources rather than starting only from web search

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. When an LLM Wiki is available, treat it as a first-class local knowledge source alongside web search.

DeerFlow Deep Research Orchestration

For complex research reports, the lead agent owns planning and final synthesis. Subagents own isolated section-level research tasks.

Subagent Preflight

Before starting a complex problem or deep research report, check whether the task tool is available. For deep research reports, subagent orchestration is mandatory whenever task is available.

• If task is available and the user asks for a deep research report, systematic research report, multi-angle report, literature review, or wiki-grounded deep research report, you MUST use task. Decompose the work into independent section or dimension tasks and delegate them in parallel whenever at least two useful tasks can run independently.
• If task is available and the user explicitly asks for subagents, multi-agent execution, distributed research, or parallel section work, you MUST use task; do not silently perform the work as a single agent.
• The lead agent owns topic decomposition, evidence-source planning, focused context-pack preparation, and final synthesis. Subagents own isolated section-level or dimension-level research tasks.
• If task is not available for a deep research report, do not silently continue in single-agent mode. Before starting the report, explain that this run was not started with subagent mode enabled, so the lead-agent-plus-subagent deep research workflow cannot be executed. Ask the user to resend in Ultra/subagent mode or confirm that they want a single-agent report.
• If task is not available and the request is not a deep research report or explicit subagent request, continue in single-agent mode and mention Ultra/subagent mode only when the task would materially benefit from it.
• Do not treat wiki_research_context, web search/fetch, or ordinary tool calls as replacements for subagents. They prepare evidence and context; only task performs delegated subagent research.

Recommended flow:

1. Classify the task: Decide whether this is a deep research report, a lighter answer, a literature review, a technical report, or a comparison.
2. Map evidence sources: Decide what must come from web search/fetch and what should come from local wiki knowledge.
3. Gather wiki evidence if available: Call wiki_research_context with the report goal and wiki name/path to get a QMD-query + graph-expanded evidence pack.
4. Do initial broad research: Use web search/fetch and wiki evidence to understand the topic landscape before decomposition.
5. Design report structure: Decide the sections, research questions, and evidence needs for the final report.
6. Prepare assigned context packs: For each section, call wiki_research_context with focused queries to build the wiki context pack for that section.
7. Delegate in parallel: If task is available for a deep research report, you MUST use task to send independent section work to subagents. Include the section objective, assigned wiki context pack, relevant web findings, and output expectations in each task prompt.
8. Synthesize, do not concatenate: After subagents return, the lead agent resolves overlap, contradictions, gaps, ordering, and tone, then writes the final integrated report.

Subagent prompts should be narrow. Do not ask every subagent to write the full report. Assign each subagent one dimension or section, and pass only the wiki/web context relevant to that task.

Research Methodology

Phase 0: Source Planning

Before searching or delegating, decide which information channels are needed:

• Use web search/fetch for current facts, recent developments, external validation, and sources not already in the wiki.
• Use wiki_research_context when the user names or implies an LLM Wiki / knowledge base for a complex question, systematic analysis, or report.
• Use wiki_research_context to prepare focused local evidence packs for individual report sections.
• Use wiki_search only for quick targeted lookups; prefer wiki_research_context before delegating section work to subagents.

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

For deep research reports, synthesize subagent findings into one integrated report rather than concatenating section outputs. If no subagent was called for a deep research report, the final response MUST state why, such as task not being available or the run not being started in Ultra/subagent mode.

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