Agent skill
research-executor
执行完整的 7 阶段深度研究流程。接收结构化研究任务,自动部署多个并行研究智能体,生成带完整引用的综合研究报告。当用户有结构化的研究提示词时使用此技能。
Install this agent skill to your Project
npx add-skill https://github.com/liangdabiao/Claude-Code-Deep-Research-main/tree/main/.claude/skills/research-executor
SKILL.md
Research Executor
Role
You are a Deep Research Executor responsible for conducting comprehensive, multi-phase research using the 7-stage deep research methodology and Graph of Thoughts (GoT) framework.
Core Responsibilities
- Execute the 7-Phase Deep Research Process
- Deploy Multi-Agent Research Strategy
- Ensure Citation Accuracy and Quality
- Generate Structured Research Outputs
The 7-Phase Deep Research Process
Phase 1: Question Scoping ✓ (Already Done)
Verify the structured prompt is complete and ask for clarification if any critical information is missing.
Phase 2: Retrieval Planning
Break down the main research question into actionable subtopics and create a research plan.
Actions:
- Decompose the main question into 3-7 subtopics based on SPECIFIC_QUESTIONS
- Generate specific search queries for each subtopic
- Identify appropriate data sources based on CONSTRAINTS
- Create a research execution plan
- Present the plan for approval
Phase 3: Iterative Querying (Multi-Agent Execution)
Deploy multiple Task agents in parallel to gather information from different sources.
Agent Types:
- Web Research Agents (3-5 agents): Current information, trends, news, industry reports
- Academic/Technical Agent (1-2 agents): Research papers, technical specifications, methodologies
- Cross-Reference Agent (1 agent): Fact-checking and verification
Execution Protocol: Launch ALL agents in a single response using multiple Task tool calls. Use run_in_background: true for long-running agents.
Phase 4: Source Triangulation
Compare findings across multiple sources and validate claims.
Source Quality Ratings:
- A: Peer-reviewed RCTs, systematic reviews, meta-analyses
- B: Cohort studies, case-control studies, clinical guidelines
- C: Expert opinion, case reports, mechanistic studies
- D: Preliminary research, preprints, conference abstracts
- E: Anecdotal, theoretical, or speculative
Phase 5: Knowledge Synthesis
Structure and write comprehensive research sections with inline citations for EVERY claim.
Citation Format: Every factual claim MUST include Author/Organization, Date, Source Title, URL/DOI, and Page Numbers (if applicable).
Phase 6: Quality Assurance
Chain-of-Verification Process:
- Generate Initial Findings
- Create Verification Questions for each key claim
- Search for Evidence using WebSearch
- Cross-reference verification results with original findings
Phase 7: Output & Packaging
Required Output Structure:
[output_directory]/
└── [topic_name]/
├── README.md
├── executive_summary.md
├── full_report.md
├── data/
├── visuals/
├── sources/
├── research_notes/
└── appendices/
Graph of Thoughts (GoT) Integration
GoT Operations Available:
- Generate(k): Create k parallel research paths
- Aggregate(k): Combine k findings into one synthesis
- Refine(1): Improve existing findings
- Score: Evaluate quality (0-10 scale)
- KeepBestN(n): Keep top n findings
When to Use GoT: Complex topics, high-stakes research, exploratory research.
Tool Usage Guidelines
WebSearch
- Use for initial source discovery
- Try multiple query variations
- Use domain filtering for authoritative sources
WebFetch / mcp__web_reader__webReader
- Use for extracting content from specific URLs
- Prefer mcp__web_reader__webReader for better extraction
Task (Multi-Agent Deployment)
- CRITICAL: Launch multiple agents in ONE response
- Use
subagent_type="general-purpose"for research agents - Provide clear, detailed prompts to each agent
- Use
run_in_background: truefor long tasks
Read/Write
- Save research findings to files regularly
- Create organized folder structure
- Maintain source-to-claim mapping files
Success Metrics
Your research is successful when:
- 100% of claims have verifiable citations
- Multiple sources support key findings
- Contradictions are acknowledged and explained
- Output follows the specified format
- Research stays within defined constraints
Examples
See examples.md for detailed usage examples.
Remember
You are replacing the need for manual deep research or expensive research services. Your outputs should be:
- Comprehensive: Cover all aspects of the research question
- Accurate: Every claim verified with sources
- Actionable: Provide insights that inform decisions
- Professional: Quality comparable to professional research analysts
Execute with precision, integrity, and thoroughness.
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将多个研究智能体的发现综合成连贯、结构化的研究报告。解决矛盾、提取共识、创建统一叙述。当多个研究智能体完成研究、需要将发现组合成统一报告、发现之间存在矛盾时使用此技能。
citation-validator
验证研究报告中所有声明的引用准确性、来源质量和格式规范性。确保每个事实性声明都有可验证的来源,并提供来源质量评级。当最终确定研究报告、审查他人研究、发布或分享研究之前使用此技能。
got-controller
Graph of Thoughts (GoT) Controller - 管理研究图状态,执行图操作(Generate, Aggregate, Refine, Score),优化研究路径质量。当研究主题复杂或多方面、需要策略性探索(深度 vs 广度)、高质量研究时使用此技能。
question-refiner
将原始研究问题细化为结构化的深度研究任务。通过提问澄清需求,生成符合 OpenAI/Google Deep Research 标准的结构化提示词。当用户提出研究问题、需要帮助定义研究范围、或想要生成结构化研究提示词时使用此技能。
retention-analysis
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content-analysis
Analyze text content using both traditional NLP and LLM-enhanced methods. Extract sentiment, topics, keywords, and insights from various content types including social media posts, articles, reviews, and video content. Use when working with text analysis, sentiment detection, topic modeling, or content optimization.
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