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bird-frank

Deep Research

by bird-frank · GitHub ↗ · v0.1.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install deep-research-plan
Description
Automated deep research that performs comprehensive multi-source investigation and produces detailed reports with citations. Use when user requests research,...
README (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.

Usage Guidance
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.
Capability Analysis
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.
Capability Assessment
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.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install deep-research-plan
  3. After installation, invoke the skill by name or use /deep-research-plan
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
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.
Metadata
Slug deep-research-plan
Version 0.1.0
License MIT-0
All-time Installs 2
Active Installs 2
Total Versions 1
Frequently Asked Questions

What is Deep Research?

Automated deep research that performs comprehensive multi-source investigation and produces detailed reports with citations. Use when user requests research,... It is an AI Agent Skill for Claude Code / OpenClaw, with 267 downloads so far.

How do I install Deep Research?

Run "/install deep-research-plan" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Deep Research free?

Yes, Deep Research is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Deep Research support?

Deep Research is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Deep Research?

It is built and maintained by bird-frank (@bird-frank); the current version is v0.1.0.

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