web-research

作者: langchain-ai

使用此技能處理與網路研究相關的請求;它提供了一種結構化方法來進行全面的網路研究

npx skills add https://github.com/langchain-ai/deepagentsjs --skill web-research

Web Research Skill

This skill provides a structured approach to conducting comprehensive web research using the task tool to spawn research subagents. It emphasizes planning, efficient delegation, and systematic synthesis of findings.

When to Use This Skill

Use this skill when you need to:

  • Research complex topics requiring multiple information sources
  • Gather and synthesize current information from the web
  • Conduct comparative analysis across multiple subjects
  • Produce well-sourced research reports with clear citations

Research Process

Step 1: Create and Save Research Plan

Before delegating to subagents, you MUST:

  1. Create a research folder - Organize all research files in a dedicated folder relative to the current working directory:

    mkdir research_[topic_name]
    

    This keeps files organized and prevents clutter in the working directory.

  2. Analyze the research question - Break it down into distinct, non-overlapping subtopics

  3. Write a research plan file - Use the write_file tool to create research_[topic_name]/research_plan.md containing:

    • The main research question
    • 2-5 specific subtopics to investigate
    • Expected information from each subtopic
    • How results will be synthesized

Planning Guidelines:

  • Simple fact-finding: 1-2 subtopics
  • Comparative analysis: 1 subtopic per comparison element (max 3)
  • Complex investigations: 3-5 subtopics

Step 2: Delegate to Research Subagents

For each subtopic in your plan:

  1. Use the task tool to spawn a research subagent with:

    • Clear, specific research question (no acronyms)
    • Instructions to write findings to a file: research_[topic_name]/findings_[subtopic].md
    • Budget: 3-5 web searches maximum
  2. Run up to 3 subagents in parallel for efficient research

Subagent Instructions Template:

Research [SPECIFIC TOPIC]. Use the web_search tool to gather information.
After completing your research, use write_file to save your findings to research_[topic_name]/findings_[subtopic].md.
Include key facts, relevant quotes, and source URLs.
Use 3-5 web searches maximum.

Step 3: Synthesize Findings

After all subagents complete:

  1. Review the findings files that were saved locally:

    • First run list_files research_[topic_name] to see what files were created
    • Then use read_file with the file paths (e.g., research_[topic_name]/findings_*.md)
    • Important: Use read_file for LOCAL files only, not URLs
  2. Synthesize the information - Create a comprehensive response that:

    • Directly answers the original question
    • Integrates insights from all subtopics
    • Cites specific sources with URLs (from the findings files)
    • Identifies any gaps or limitations
  3. Write final report (optional) - Use write_file to create research_[topic_name]/research_report.md if requested

Note: If you need to fetch additional information from URLs, use the fetch_url tool, not read_file.

Available Tools

You have access to:

  • write_file: Save research plans and findings to local files
  • read_file: Read local files (e.g., findings saved by subagents)
  • list_files: See what local files exist in a directory
  • fetch_url: Fetch content from URLs and convert to markdown (use this for web pages, not read_file)
  • task: Spawn research subagents with web_search access

Research Subagent Configuration

Each subagent you spawn will have access to:

  • web_search: Search the web using Tavily (parameters: query, max_results, topic, include_raw_content)
  • write_file: Save their findings to the filesystem

Best Practices

  • Plan before delegating - Always write research_plan.md first
  • Clear subtopics - Ensure each subagent has distinct, non-overlapping scope
  • File-based communication - Have subagents save findings to files, not return them directly
  • Systematic synthesis - Read all findings files before creating final response
  • Stop appropriately - Don't over-research; 3-5 searches per subtopic is usually sufficient

來自 langchain-ai 的更多技能

deepagents-thread-inspector
langchain-ai
檢查並解釋本地 Deep Agents Code SQLite 工作階段儲存庫中的對話。當 LangSmith 追蹤工具不可用時作為備用方案,用於…
deepagents-python-quickstart
langchain-ai
按照官方快速入門,在 Python 中搭建一個最小的本地 Deep Agent,使用提供者原生的網路搜尋而非 Tavily。當使用者想要……時使用。
deepagents-typescript-quickstart
langchain-ai
按照官方快速入門指南,以 TypeScript 搭建一個最小的本地 Deep Agent,使用供應商原生的網路搜尋而非 Tavily。當使用者……時使用
eval-engineering
langchain-ai
反覆檢查 agent 儲存庫與使用者提供的可選追蹤資料,訪談使用者,並逐一建立、執行及稽核 Harbor evals。用於……
LangChain RAG Pipeline
langchain-ai
在構建任何檢索增強生成(RAG)系統時,請調用此技能。涵蓋文檔加載器、遞迴字符文本分割器、嵌入(OpenAI)等。
LangChain Structured Output & HITL
langchain-ai
langchain-structured-output-&-hitl — 一個可安裝的 AI 代理技能,由 langchain-ai/langchain-skills 發布。
LangSmith Datasets
langchain-ai
當從追蹤建立評估資料集,或將資料集上傳至 LangSmith,或查詢資料集時,請調用此技能。涵蓋資料集類型(final_response、…)
langsmith-evaluator
langchain-ai
在為 LangSmith 建立評估管道時,請調用此技能。涵蓋三個核心組件:(1) 建立評估器 - LLM 作為評審、自訂程式碼;(2)…