deep-research-main

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:

1. Extract the topic from user's message
2. 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.

EXECUTE: 아래 JSON으로 AskUserQuestion 도구를 즉시 호출한다 (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 (EXECUTE):

{
  "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"}
      ],
      "multiSelect": false
    }
  ]
}

3. 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

1. Always append year to queries:

   • BAD: "AI code assistants market"
   • GOOD: "AI code assistants market 2026"
   • GOOD: "AI code assistants trends February 2026"

2. Use recency operators:

   • "after:2025" for Google
   • "since:2025" for news
   • "2025..2026" for date ranges

3. Add freshness keywords:

   • "latest", "recent", "current", "new"
   • "[current year] update"

4. 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

5. 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
  • WebFetch 실패 시 → tool_strategy.md의 플랫폼별 접근 전략 또는 Fallback 순서대로 시도
  • 우회 성공 시 소스 신뢰도에 via_fallback 태그 추가
  • 실패한 URL과 우회 시도 결과를 sources/failed_urls.txt에 함께 기록
• 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 with mode: "bypassPermissions". 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

기본 도구(WebSearch, WebFetch, Bash/curl)로 리서치를 수행한다. 플랫폼별 최적 접근법은 tool_strategy.md를 참조한다. 환경에 MCP 도구(Perplexity, Firecrawl, Exa 등)가 설치되어 있으면 우선 활용하되, 없어도 기본 도구만으로 충분한 리서치가 가능하다.

Deploy parallel research agents using the Task tool with run_in_background=True and mode: "bypassPermissions" 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

1. Author/Organization - Who made this claim
2. Date - When published
3. Source Title - Name of paper, article, or report
4. URL/DOI - Direct link to verify
5. 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

1. Always ground statements in source material

   • Never claim without a verifiable source
   • If uncertain, state "Source needed" rather than guessing

2. Use Chain-of-Verification for critical claims

   • Generate verification questions
   • Search for answers independently
   • Only finalize when verified

3. Cross-reference multiple sources

   • Key findings need 2+ independent sources
   • Note when sources disagree

4. Explicitly state uncertainty

   • "According to [source]..." not "Studies show..."
   • Qualify preliminary or contested findings

Verification Checklist

• Every claim has inline citation
• All URLs are accessible
• No orphan citations
• Contradictions acknowledged
• Source quality ratings applied

---

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": [], "min_quality": "B"},
    "audience": "executive",
    "special_requirements": []
  },
  "plan": {
    "subtopics": [],
    "search_queries": {},
    "agent_assignments": []
  },
  "progress": {
    "phase_1": "completed",
    "phase_2": "completed",
    "phase_3": "in_progress",
    "phase_4": "pending",
    "phase_5": "pending",
    "phase_6": "pending",
    "phase_7": "pending"
  },
  "sources_count": 0,
  "artifacts": {},
  "errors": []
}

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

---

Research Type 기반 골격 동적 생성 (참고용 — 기본 5섹션은 그대로 유지)

기본 5섹션 골격(introduction/landscape/challenges/future_outlook/conclusions)이 모든 리서치의 default. 사용자가 명시적으로 다른 type을 요청한 경우, 아래 참고 예시 패턴을 보고 사용자 리서치에 맞게 골격을 즉석 동적 생성한다.

주의: 기본 7-Phase + 5섹션 + Date-aware는 모두 deep-research의 핵심 contract로 보존. 본 type별 골격은 사용자 명시 요청 시에만 적용되는 advanced 옵션이며, 표는 카탈로그 메뉴가 아니라 동적 생성 학습용 예시다.

동적 생성 원칙

• 사용자 리서치 핵심 → 5 섹션 슬롯 채우기: 도입(introduction) / 핵심 분석 / 비교/예측/원인 등 도메인 특화 / 한계와 위험 / 결론
• 같은 type이라도 사용자 주제에 따라 섹션 명을 다르게 (단순 카피 금지)
• 표의 섹션 명은 그대로 사용하지 말고, 사용자 주제에 맞는 명칭으로 변환

참고 예시 (메뉴 아님 — 패턴 학습용)

Research Type	5섹션 패턴 예시	적합 사례
Exploratory (새 영역 탐색)	introduction / landscape / opportunities / challenges / conclusions	신규 시장/기술 탐색
Comparative (A vs B 비교)	introduction / criteria / comparison_matrix / recommendation / conclusions	도구/제품 비교
Predictive (미래 시나리오)	introduction / current_state / trends / scenarios / risks_and_recommendations	시장 예측 / 기술 로드맵
Analytical (원인-결과)	introduction / problem / causes / effects / conclusions	사건 분석 / 인과 추적
기본 (Generic)	introduction / current_landscape / challenges / future_outlook / conclusions	종합 리서치 (default)

→ 위는 패턴 학습용 예시. 사용자 주제가 "X 시장의 한국 vs 일본 차이"면 Comparative 패턴으로 introduction / 시장규모비교 / 사용자행동차이 / 규제차이 / 진입전략추천 같이 섹션 명을 즉석 변환.

적용 절차

1. Phase 1 (Question Scoping)에서 사용자 자연어로부터 리서치 type 추정 (Exploratory / Comparative / Predictive / Analytical / Generic 패턴 중 가장 가까운 것)
2. 위 예시 패턴을 학습한 후, 사용자 주제에 맞춰 5 섹션 명을 동적 생성
3. 사용자에게 confirm: "이 리서치는 [Comparative] 패턴에 가까워 보입니다. 5섹션을 [introduction / X 비교 기준 / X vs Y 비교 / 추천 / 결론]으로 진행할까요? 또는 기본 5섹션으로?"
4. 사용자 confirm → 동적 생성된 골격 사용 / 사용자 미명시 또는 모호 → 기본 5섹션 사용 (안전 default)
5. state.json report_skeleton 필드에 최종 결정된 골격 기록 (resume 가능)

⚠️ 주의사항

• type 자동 결정 금지 — 사용자 confirm 필수
• 위 표는 메뉴가 아닌 패턴 학습용 예시 — 섹션 명을 그대로 카피하지 말고 사용자 주제에 맞춰 변환
• 7-Phase / minimum 2 sources / A-E quality / Hallucination Prevention 등 결정 contract는 모두 그대로 유지
• 본 골격 동적 생성은 advanced 옵션이며, 기본 동작은 5섹션 그대로
• 새 type 사례를 본 표에 추가하지 말 것 — 이 표는 카탈로그가 아닌 패턴 예시집

---

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:

1. List available sessions: RESEARCH/*/state.json
2. Load selected session's state.json
3. Check progress object for last completed phase
4. Resume from next pending phase
5. 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

1. Log error to state.json errors array
2. Mark phase as failed in progress
3. Notify user with details
4. Offer: Retry / Skip / Abort

Network Failures

• Retry up to 3 times with backoff
• If still failing → tool_strategy.md의 "접근 불가 시 우회 전략 (Fallback)" 참조
  • 모바일 UA curl → OGP 메타태그 → Google 캐시/Wayback → curl_cffi → Playwright MCP
• 응답 검증 규칙으로 성공/실패 판정 (로그인 페이지, CAPTCHA, 빈 SPA 감지)
• Log failed URLs + fallback attempt results to sources/failed_urls.txt
• Continue with available sources (including fallback-retrieved content)

Token Limits

• Split long documents into chunks
• Save intermediate results frequently
• Use summarization for very long sources

---

Quality Checklist (Before Completion)

• Every claim has a verifiable source
• Multiple sources corroborate key findings
• Contradictions are acknowledged and explained
• Sources are recent and authoritative
• No hallucinations or unsupported claims
• Clear logical flow from evidence to conclusions
• Proper citation format throughout
• Executive summary reflects full content
• Bibliography is complete
• All background agents completed and results collected

---

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