web-research

作成者: langchain-ai

このスキルはウェブリサーチに関連するリクエストに使用します。包括的なウェブリサーチを実施するための構造化されたアプローチを提供します。

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

langchain-aiのその他のスキル

deepagents-thread-inspector
langchain-ai
ローカルのDeep Agents Code SQLiteセッションストア内の会話を検査・説明します。LangSmithトレースツールが利用できない場合のフォールバックとして使用し、…
deepagents-python-quickstart
langchain-ai
公式クイックスタートに従って、Pythonで最小限のローカルDeep Agentをスキャフォールドし、Tavilyの代わりにプロバイダー標準のウェブ検索を使用します。ユーザーが…を望む場合に使用します。
deepagents-typescript-quickstart
langchain-ai
公式クイックスタートに従って、Tavilyの代わりにプロバイダー標準のウェブ検索を使用しながら、TypeScriptで最小限のローカルDeep Agentをスキャフォールドする。ユーザーが…の場合に使用する。
eval-engineering
langchain-ai
エージェントリポジトリと、ユーザーが提供した任意のトレースを反復的に調査し、ユーザーにインタビューし、Harborの評価を一度に1つ作成・実行・監査します。~に使用します…
LangChain RAG Pipeline
langchain-ai
このスキルは、あらゆる検索拡張生成(RAG)システムを構築する際に呼び出してください。ドキュメントローダー、RecursiveCharacterTextSplitter、埋め込み(OpenAI)などをカバーします。
LangChain Structured Output & HITL
langchain-ai
langchain-structured-output-&-hitl — AIエージェント用のインストール可能なスキルで、langchain-ai/langchain-skillsから公開されています。
LangSmith Datasets
langchain-ai
このスキルは、トレースから評価データセットを作成する場合、データセットをLangSmithにアップロードする場合、またはデータセットをクエリする場合に呼び出します。データセットの種類(final_response、…)をカバーします。
langsmith-evaluator
langchain-ai
LangSmithの評価パイプラインを構築する際にこのスキルを呼び出してください。以下の3つのコアコンポーネントをカバーします:(1) 評価器の作成 - LLM-as-Judge、カスタムコード;(2)…