Skill: lingchenheiye/deep-research-engine (version 0.1.0)

--- 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: ```bash 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: ```bash 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 ```

path: backend/requirements.txt
content: deepagents>=0.1.0
langchain>=0.3.0
langchain-core>=0.3.0
langchain-anthropic>=0.3.0
tavily-python>=0.5.0
httpx>=0.27.0
markdownify>=0.13.0


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)