深度研究

deep-research

by bird-frank

Automated deep research that performs comprehensive multi-source investigation and produces detailed reports with citations. Use when user requests research, investigation, or in-depth analysis of any topic. Capabilities: generate structured research plans, and start sub agent to execute the plan.

3.7k搜索与获取未扫描2026年3月23日

安装

claude skill add --url github.com/openclaw/skills/tree/main/skills/bird-frank/deep-research-plan

文档

Deep Research

Two-phase research workflow: planning then execution.

Overview

This skill provides a structured approach to deep research:

Phase 1: Planning (High Freedom)

  • Discuss with user to clarify and refine research questions.
  • Define what to investigate and what the report should cover.
  • Set expectations for research depth and output.
  • Create research plan document.

Phase 2: Execution (Low Freedom)

  • Sub-agent reads the research plan
  • Independently decides how to search for each sub-question
  • Can dynamically add searches based on findings
  • Analyzes content and generates report with citations

Phase 1: Generate Research Plan

The coordinator (main session) performs:

  1. Understand the research topic — Listen to user's request and understand what they want to investigate
  2. Collaborate with user — Discuss and clarify research questions together. Present 3-5 potential sub-questions or research angles for user to review
  3. Define scope together — Discuss what to include/exclude, confirm boundaries of the research
  4. Confirm report expectations — Ask user what sections they want, what depth, any specific focus areas
  5. Get user confirmation — Present the draft plan to user and wait for approval before proceeding
  6. Output: Research plan document — Only after user confirms, save to plans/research-plan-{timestamp}.json

Key principle: The plan is a collaboration between coordinator and user. Never proceed to Phase 2 without explicit user confirmation of the research plan.

Research plan format (JSON):

json
{
  "topic": "Original research topic",
  "research_questions": [
    "What are the latest breakthroughs in this field?",
    "Who are the leading organizations or researchers?",
    "What are the current limitations or challenges?",
    "What are the practical applications?"
  ],
  "scope": {
    "include": ["recent developments", "key players", "technical details"],
    "exclude": ["historical background before 2020", "unrelated applications"]
  },
  "report_requirements": {
    "sections": ["executive_summary", "findings", "conclusion", "references"],
    "depth": "comprehensive",
    "min_sources": 8,
    "focus_areas": ["technical analysis", "market landscape"]
  }
}

Research Plan Schema

Required fields:

  • topic: Original research topic
  • research_questions: Array of questions to investigate
  • report_requirements: Object specifying output expectations

Optional fields:

  • scope: Define boundaries of research (include/exclude)
  • min_sources: Minimum sources to analyze (default: 8)
  • max_sources: Maximum sources to analyze (default: 20)
  • notes: Additional context or special instructions

Save plan to: plans/research-plan-{timestamp}.json

⚠️ WAIT FOR USER CONFIRMATION — Do not proceed to Phase 2 until user explicitly approves the research plan.

Key principle: The plan defines WHAT to research and WHAT the output should contain. It does NOT specify HOW to search (keywords, sources, rounds) - that is up to the research agent to determine dynamically.

Phase 2: Execute Research Plan

Launch sub agent with the research plan. Launch sub agent with session_spawn tool. Instruct subagent to use deep-research-executor to execute the plan EXPLICITLY.

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