Deep Research Agent

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.

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