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Deep Research

作者 bird-frank · GitHub ↗ · v0.1.0 · MIT-0
cross-platform ✓ 安全检测通过
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当前安装
1
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在 OpenClaw 中安装
/install deep-research-plan
功能描述
Automated deep research that performs comprehensive multi-source investigation and produces detailed reports with citations. Use when user requests research,...
使用说明 (SKILL.md)

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):

{
  "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.

安全使用建议
This skill appears coherent and doesn't ask for credentials or installs, but before enabling it consider: 1) The skill will spawn a sub-agent that can browse external sources and create files (plans/*.json). Confirm you are comfortable with that agent's web/network/file permissions. 2) The SKILL.md references a tool ('deep-research-executor') and the session_spawn capability — verify those tools exist and what privileges they have on your platform. 3) Keep an eye on the created plans/ files and on outbound queries the sub-agent performs (to avoid accidental data leakage). 4) If you want tighter control, require the coordinator to present the final list of exact sources or deny session_spawn/autonomous browsing before allowing Phase 2.
功能分析
Type: OpenClaw Skill Name: bf-deep-research Version: 0.1.0 The skill implements a structured two-phase research workflow (planning and execution) for an AI agent. It emphasizes user collaboration, requires explicit confirmation before spawning sub-agents, and uses standard JSON formats for research plans. No indicators of malicious intent, data exfiltration, or unauthorized execution were found in SKILL.md or the supporting files.
能力评估
Purpose & Capability
The name/description (deep research, plan + execute) match the SKILL.md steps. The skill requests no binaries, env vars, or installs that would be unrelated to research.
Instruction Scope
Phase 1 is collaborative and must wait for explicit user confirmation (good). Phase 2 instructs the coordinator to spawn a sub-agent (session_spawn) and let it 'independently decide how to search' and 'dynamically add searches' — this is coherent for autonomous research but grants the spawned agent broad discretion to access web sources and create/collect data. The SKILL.md also directs saving plans to plans/research-plan-{timestamp}.json and references a tool name 'deep-research-executor' that is not included here (availability is unknown).
Install Mechanism
Instruction-only skill with no install spec and no code files. Lowest install risk. It will write research-plan JSON files to a local plans/ path during normal operation.
Credentials
No environment variables, credentials, or config paths are required or requested; that is proportionate to an instruction-only research coordinator.
Persistence & Privilege
always:false and user-invocable (normal). However the skill instructs creation of sub-agents via session_spawn which increases the operational blast radius depending on the platform's sub-agent privileges. The skill itself does not request persistent system-wide privileges.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install deep-research-plan
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /deep-research-plan 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v0.1.0
- Initial release of the deep-research skill. - Supports a two-phase workflow: collaborative research plan creation, followed by automated execution. - Guides users through clarifying research questions, defining scope, and confirming report expectations. - Saves detailed research plans in structured JSON format with user approval before execution. - Launches a sub-agent to perform research and generate a comprehensive, cited report based on the approved plan.
元数据
Slug deep-research-plan
版本 0.1.0
许可证 MIT-0
累计安装 2
当前安装数 2
历史版本数 1
常见问题

Deep Research 是什么?

Automated deep research that performs comprehensive multi-source investigation and produces detailed reports with citations. Use when user requests research,... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 267 次。

如何安装 Deep Research?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install deep-research-plan」即可一键安装,无需额外配置。

Deep Research 是免费的吗?

是的,Deep Research 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Deep Research 支持哪些平台?

Deep Research 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Deep Research?

由 bird-frank(@bird-frank)开发并维护,当前版本 v0.1.0。

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