| name | deep-research-main |
| description | This skill should be used when a user requests deep research on any topic. Example queries include "/deep-research", "deep research on", "리서치해줘", "딥리서치", "심층 연구", "[주제]에 대해 리서치해줘". |
Deep Research Skill
AI-powered comprehensive research with state management, multi-agent source verification, and structured outputs.
Trigger Conditions
# Primary triggers
- "/deep-research [topic]"
- "/research [topic]"
- "딥리서치 [주제]"
- "심층 연구 [주제]"
- "[주제]에 대해 리서치해줘"
- "[주제] 리서치"
- "deep research on [topic]"
# Resume triggers
- "/deep-research resume [session_id]"
- "/research-resume [session_id]"
# Status triggers
- "/deep-research status"
- "/research-status"
WHEN TRIGGERED - EXECUTE IMMEDIATELY
DO NOT just display this documentation. EXECUTE the research flow immediately.
On Trigger Action:
- Extract the topic from user's message
- Start Phase 1 - Use
AskUserQuestion tool for interactive selection
CRITICAL REQUIREMENT
CALL THE AskUserQuestion TOOL IMMEDIATELY.
DO NOT output text-based questions. INSTEAD, call the AskUserQuestion tool with JSON parameters.
Language Detection
- Detect the language of user's input (topic query)
- Generate ALL question labels and descriptions in the SAME LANGUAGE as user input
- If Korean -> Korean options, If English -> English options, etc.
Use AskUserQuestion with these questions (combine into 1-4 question groups).
Translate all labels/descriptions to match user's language:
English Example:
{
"questions": [
{
"question": "What aspects interest you most?",
"header": "Focus",
"options": [
{"label": "Current state & trends", "description": "Latest developments, market status, key players"},
{"label": "Technical deep-dive", "description": "Architecture, implementation, tech stack"},
{"label": "Market analysis", "description": "Market size, growth rate, competition"},
{"label": "All of the above (Recommended)", "description": "Comprehensive research - all aspects"}
],
"multiSelect": false
},
{
"question": "What type of deliverable do you want?",
"header": "Output",
"options": [
{"label": "Comprehensive report (Recommended)", "description": "20-50+ pages, detailed analysis and insights"},
{"label": "Executive summary", "description": "3-5 pages, key points only"},
{"label": "Modular documents", "description": "Multiple documents by topic"}
],
"multiSelect": false
},
{
"question": "Who will read this research?",
"header": "Audience",
"options": [
{"label": "Technical team/Developers", "description": "Include technical details"},
{"label": "Business executives", "description": "Focus on strategic insights"},
{"label": "Researchers/Academic", "description": "Academic citations and methodology"},
{"label": "General audience", "description": "Easy explanations and overview"}
],
"multiSelect": false
},
{
"question": "Any source preferences?",
"header": "Sources",
"options": [
{"label": "Academic/Papers", "description": "Peer-reviewed papers, conferences"},
{"label": "Industry reports", "description": "Gartner, white papers, analyst reports"},
{"label": "News/Current", "description": "Media, blogs, latest announcements"},
{"label": "All sources (Recommended)", "description": "All reliable sources"}
],
"multiSelect": false
}
]
}
Korean Example:
{
"questions": [
{
"question": "어떤 측면에 관심이 있으신가요?",
"header": "Focus",
"options": [
{"label": "현재 상태와 트렌드", "description": "최신 동향, 시장 현황, 주요 플레이어"},
{"label": "기술 심층 분석", "description": "아키텍처, 구현 방법, 기술 스택"},
{"label": "시장 분석", "description": "시장 규모, 성장률, 경쟁 구도"},
{"label": "모두 포함 (Recommended)", "description": "종합 리서치 - 모든 측면 분석"}
],
- After user responds:
- Create session folder:
RESEARCH/{topic}_{timestamp}/
- Initialize
state.json
- Execute Phase 2-7 sequentially
- Use parallel background agents for searching
- Deliver final report to
outputs/ folder
The 7-Phase Deep Research Process
Phase 1: Question Scoping
- Clarify the research question with the user
- Define output format and success criteria
- Identify constraints and desired tone
- Create unambiguous query with clear parameters
Phase 2: Retrieval Planning
- Break main question into 3-5 subtopics
- Generate specific search queries per subtopic
- Select appropriate data sources
- Create research plan for user approval
- Use Graph of Thoughts to model research as operations
DATE-AWARE QUERY GENERATION (CRITICAL)
All search queries MUST include current date context for freshness.
Get Today's Date First
Before generating ANY search query, determine today's date from the system context.
Query Generation Rules
-
Always append year to queries:
- BAD: "AI code assistants market"
- GOOD: "AI code assistants market 2026"
- GOOD: "AI code assistants trends February 2026"
-
Use recency operators:
- "after:2025" for Google
- "since:2025" for news
- "2025..2026" for date ranges
-
Add freshness keywords:
- "latest", "recent", "current", "new"
- "[current year] update"
-
Example transformations:
| User Query | Generated Search Query |
|---|
| AI 코딩 어시스턴트 | AI 코딩 어시스턴트 2026 최신 동향 |
| startup trends | startup trends 2026 latest |
| React vs Vue | React vs Vue 2026 comparison |
-
For academic/historical research:
- Still include current year for "state of" queries
- Use date ranges: "climate change research 2020-2026"
Search Query Template
[topic] [current_year] [freshness_keyword] [specific_aspect]
Phase 3: Iterative Querying
- Execute searches systematically with parallel agents
- Navigate and extract relevant information
- Formulate new queries based on findings
- Use multiple search modalities (web, academic, code)
Phase 4: Source Triangulation
- Compare findings across multiple sources
- Validate claims with cross-references (minimum 2 sources for key claims)
- Handle inconsistencies and note contradictions
- Assess source credibility with A-E ratings
Phase 5: Knowledge Synthesis
- Structure content logically
- Write comprehensive sections
- Include inline citations for EVERY claim
- Add data visualizations when relevant
Phase 6: Quality Assurance
- Check for hallucinations and errors
- Verify all citations match content
- Ensure completeness and clarity
- Apply Chain-of-Verification techniques
Phase 7: Output & Packaging
- Format for optimal readability
- Include executive summary
- Create proper bibliography
- Export in requested format
- Optionally generate interactive website
Multi-Agent Research Strategy
Agent Deployment (Phase 3)
Deploy 3-5 parallel agents to maximize coverage:
| Agent Type | Count | Focus | Output |
|---|
| Web Research | 2-3 | Current info, trends, news | Structured summaries with source URLs |
| Academic/Technical | 1-2 | Papers, specs, methodology | Technical analysis with citations |
| Cross-Reference | 1 | Fact-checking, verification | Confidence ratings for key findings |
Launch multiple Task calls in a single response for parallel execution. Each agent receives a focused prompt with specific subtopic and citation requirements.
For detailed agent prompt templates and Graph of Thoughts integration:
${CLAUDE_PLUGIN_ROOT}/skills/deep-research-main/references/agent_prompts.md
Tool Usage
Use whichever search and extraction tools are available. Prioritize: MCP tools (Firecrawl, Google Search, Exa) > Built-in tools (WebSearch, WebFetch) > Specialized tools (GitHub search, library docs).
Deploy parallel research agents using the Task tool with run_in_background=True for concurrent subtopic investigation.
For detailed tool strategy and code examples:
${CLAUDE_PLUGIN_ROOT}/skills/deep-research-main/references/tool_strategy.md
Citation Requirements
Every factual claim MUST include inline citation.
Mandatory Standards
- Author/Organization - Who made this claim
- Date - When published
- Source Title - Name of paper, article, or report
- URL/DOI - Direct link to verify
- Page Numbers - For lengthy documents (when applicable)
Source Quality Ratings
| Grade | Description | Examples |
|---|
| A | Peer-reviewed, systematic reviews, meta-analyses | Nature, Lancet, IEEE |
| B | Official docs, clinical guidelines, cohort studies | FDA, W3C, WHO |
| C | Expert opinion, case reports, industry reports | Gartner, conferences |
| D | Preliminary research, preprints, white papers | arXiv, company blogs |
| E | Anecdotal, theoretical, speculative | Social media, forums |
Red Flags (Unreliable Sources)
- No author attribution
- Missing publication dates
- Broken or suspicious URLs
- Claims without data
- Conflicts of interest not disclosed
- Predatory journals
- Retracted papers
For detailed citation formatting rules, refer to:
${CLAUDE_PLUGIN_ROOT}/skills/deep-research-main/references/citation_rules.md
For complete source quality assessment rubric:
${CLAUDE_PLUGIN_ROOT}/skills/deep-research-main/references/quality_rubric.md
Hallucination Prevention
Core Strategies
-
Always ground statements in source material
- Never claim without a verifiable source
- If uncertain, state "Source needed" rather than guessing
-
Use Chain-of-Verification for critical claims
- Generate verification questions
- Search for answers independently
- Only finalize when verified
-
Cross-reference multiple sources
- Key findings need 2+ independent sources
- Note when sources disagree
-
Explicitly state uncertainty
- "According to [source]..." not "Studies show..."
- Qualify preliminary or contested findings
Verification Checklist
State Management
state.json Schema
{
"session_id": "Topic_Name_20260224_143000",
"topic": "Research Topic",
"created_at": "2026-02-24T14:30:00Z",
"updated_at": "2026-02-24T15:45:00Z",
"status": "PHASE_3_QUERYING",
"current_phase": 3,
"requirements": {
"focus": ["aspect1", "aspect2"],
"output_format": "comprehensive_report",
"scope": {"timeframe": {}, "geography": {}},
"sources": {"required_types"
sources.jsonl Schema (one JSON per line)
{"id": "src_001", "url": "https://...", "title": "Article Title", "author": "Author", "date": "2024-06-15", "domain": "nature.com", "type": "academic", "quality_rating": "A", "snippet": "relevant excerpt...", "claims": ["claim1"], "verified": true}
For detailed phase input/output contracts:
${CLAUDE_PLUGIN_ROOT}/skills/deep-research-main/references/phase_contracts.md
Output Structure
RESEARCH/{topic}_{timestamp}/
├── state.json # Session state (resumable)
├── README.md # Navigation guide
│
├── artifacts/ # Intermediate outputs
│ ├── research_plan.json
│ ├── agent_results/
│ └── drafts/
│
├── sources/
│ ├── sources.jsonl # All collected sources
│ ├── bibliography.md # Formatted citations
│ └── quality_report.md # Source quality ratings
│
├── outputs/ # FINAL DELIVERABLES
│ ├── 00_executive_summary.md
│ ├── 01_full_report/
│ │ ├── 01_introduction.md
│ │ ├── 02_current_landscape.md
│ │ ├── 03_challenges.md
│ │ ├── 04_future_outlook.md
│ │ └── 05_conclusions.md
│ ├── 02_appendices/
│ └── comparison_data.json
│
└── website/ # (optional) Visual presentation
├── index.html
├── styles.css
└── script.js
Output Templates
Use the templates at ${CLAUDE_PLUGIN_ROOT}/skills/deep-research-main/assets/templates/ for consistent formatting:
| Template | Purpose |
|---|
executive_summary.md | Executive summary structure |
full_report_section.md | Individual report section template |
bibliography.md | Bibliography with quality distribution |
readme_research.md | Research session README/navigation |
website_template.html | Interactive web presentation |
Structured Query Support
For precise research control, accept structured JSON queries following the schema at:
${CLAUDE_PLUGIN_ROOT}/skills/deep-research-main/references/query_schema.json
When a user provides a JSON object as input, parse it according to the schema and skip Phase 1 (Question Scoping) since requirements are already defined.
Example queries are available at:
${CLAUDE_PLUGIN_ROOT}/skills/deep-research-main/examples/
Resume Protocol
When resume is triggered:
- List available sessions:
RESEARCH/*/state.json
- Load selected session's
state.json
- Check
progress object for last completed phase
- Resume from next pending phase
- Continue execution loop
for phase_num in range(1, 8):
phase_key = f"phase_{phase_num}"
if state["progress"][phase_key] == "in_progress":
resume_phase(phase_num)
break
elif state["progress"][phase_key] == "pending":
start_phase(phase_num)
break
Error Handling
Phase Failures
- Log error to
state.json errors array
- Mark phase as
failed in progress
- Notify user with details
- Offer: Retry / Skip / Abort
Network Failures
- Retry up to 3 times with backoff
- Log failed URLs to
sources/failed_urls.txt
- Continue with available sources
Token Limits
- Split long documents into chunks
- Save intermediate results frequently
- Use summarization for very long sources
Quality Checklist (Before Completion)
Scripts and Utilities
State management scripts are available at:
${CLAUDE_PLUGIN_ROOT}/skills/deep-research-main/scripts/
| Script | Purpose |
|---|
orchestrator.py | Research state machine controller - session creation, phase management, source tracking |
pipelines.py | Pipeline definitions - agent prompts, clarification templates, synthesis prompts |
These can be executed via Bash to initialize sessions or manage state programmatically.
References
For detailed documentation on specific aspects:
| Reference | Location |
|---|
| Citation formatting rules | ${CLAUDE_PLUGIN_ROOT}/skills/deep-research-main/references/citation_rules.md |
| Phase input/output contracts | ${CLAUDE_PLUGIN_ROOT}/skills/deep-research-main/references/phase_contracts.md |
| Source quality rubric | ${CLAUDE_PLUGIN_ROOT}/skills/deep-research-main/references/quality_rubric.md |
| Agent prompt templates & GoT | ${CLAUDE_PLUGIN_ROOT}/skills/deep-research-main/references/agent_prompts.md |
| Tool strategy & code examples | ${CLAUDE_PLUGIN_ROOT}/skills/deep-research-main/references/tool_strategy.md |
| Structured query schema | ${CLAUDE_PLUGIN_ROOT}/skills/deep-research-main/references/query_schema.json |
| Query generation guide | ${CLAUDE_PLUGIN_ROOT}/skills/deep-research-main/references/query_generator.md |
