
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。
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/liangdabiao/claude-code-stock-deep-research-agent --skill research-executor- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads research-executor automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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.
FAQ
What does the research-executor skill do?
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。
How do I install research-executor?
Run npx skills add https://github.com/liangdabiao/claude-code-stock-deep-research-agent --skill research-executor in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/research-executor/ (personal) or .claude/skills/research-executor/ (project).
Which AI agents can use research-executor?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is research-executor safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains research-executor?
It is published by liangdabiao in liangdabiao/claude-code-stock-deep-research-agent. It has 114 recorded installs.
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