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🦀 ClawHub

Deep Research Agent

by @lingchenheiye

Autonomous deep research agent with multi-step web search, sub-agent delegation, and structured report generation. Triggered by requests for deep research, 深...

Versionv0.1.0
Downloads287
#research#legal#automation
TERMINAL
clawhub install deep-research-engine

📖 About This Skill

name: deep-research-engine description: Autonomous deep research agent with multi-step web search, sub-agent delegation, and structured report generation. Triggered by requests for deep research, 深度研究, literature review, or comprehensive topic analysis. author: ClawX version: 0.1.0 dependencies: - pip: deepagents - pip: tavily-python - pip: langchain-anthropic - pip: markdownify

Deep Research Agent

When to Use

Trigger this skill when the user asks for:

  • 深度研究 / deep research on any topic
  • Comprehensive topic analysis with citations
  • Literature review or academic research
  • "Research [X]" where a thorough, multi-source report is needed
  • Comparison reports (products, technologies, methodologies)
  • Market research or competitive analysis

    • NOT for quick lookups — use web_search for simple questions.

    Prerequisites

    1. Tavily API key (free): https://tavily.com/ 2. LLM API key: Anthropic, Google, or OpenAI

    Set environment variables before first use: 

    export TAVILY_API_KEY="your_key"
export ANTHROPIC_API_KEY="your_key"  # or GOOGLE_API_KEY / OPENAI_API_KEY

    Workflow

    When triggered, follow this deep research process:

    Phase 1: Plan 📋

    1. Analyze the research question 2. Break it down into 2-5 focused sub-topics 3. Create a research plan with specific tasks

    Phase 2: Search 🔍

    1. For each sub-topic, use web_search tool to discover key information 2. Use web_fetch to read important pages in full 3. Take notes on key findings from each source 4. If a sub-topic yields insufficient info, refine search queries

    Phase 3: Synthesize 📝

    1. Consolidate findings from all sources 2. Identify contradictions or gaps 3. Form evidence-based conclusions 4. Generate inline citations for all claims

    Phase 4: Report 📄

    Output a structured report with:
  • Executive Summary — Key findings at a glance
  • Background — Context and definitions
  • Detailed Analysis — Evidence-backed exploration
  • Comparison/Insights (if applicable)
  • Conclusion — Actionable takeaways
  • Sources — Numbered list of all references (inline [1], [2], etc.)

    • Alternative: Python Backend

    For truly deep research (autonomous multi-hour sessions with Tavily), use the bundled Python script:

    cd deep-research-agent/backend
pip install -r requirements.txt
python agent.py "Research topic here"

    This spawns sub-agents for parallel research and writes /final_report.md.

    Prompt Template (Substitute & Execute)

    For quick in-session deep research (no backend needed), follow this prompt structure:

    Perform deep research on: "{user_query}"
    Research Guidelines:
1. Use web_search with at least 3 different query variations
2. Read at least 5 sources thoroughly via web_fetch
3. Cross-reference claims across sources
4. Cite inline with [1], [2], etc.
5. Note confidence levels for uncertain claims
6. Write a comprehensive report with sections

    ⚡ When to Use

    TriggerAction
    - 深度研究 / deep research on any topic
    - Comprehensive topic analysis with citations
    - Literature review or academic research
    - "Research [X]" where a thorough, multi-source report is needed
    - Comparison reports (products, technologies, methodologies)
    - Market research or competitive analysis
    **NOT** for quick lookups — use web_search for simple questions.

    ⚙️ Configuration

    1. Tavily API key (free): https://tavily.com/ 2. LLM API key: Anthropic, Google, or OpenAI

    Set environment variables before first use: 

    export TAVILY_API_KEY="your_key"
export ANTHROPIC_API_KEY="your_key"  # or GOOGLE_API_KEY / OPENAI_API_KEY