jilinchenandClaude Opus 4.5
066e22fb1d
🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
83 lines
2.6 KiB
Markdown
83 lines
2.6 KiB
Markdown
# Deep Research Skill for Claude Code
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[English](README.md) | [中文](README.zh.md)
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> Inspired by [RhinoInsight: Improving Deep Research through Control Mechanisms for Model Behavior and Context](https://arxiv.org/abs/2511.18743)
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A structured research workflow skill for Claude Code, supporting two-phase research: outline generation (extensible) and deep investigation. Human-in-the-loop design ensures precise control at every stage.
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## Use Cases
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- **Academic Research**: Paper surveys, benchmark reviews, literature analysis
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- **Technical Research**: Technology comparison, framework evaluation, tool selection
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- **Market Research**: Competitor analysis, industry trends, product comparison
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- **Due Diligence**: Company research, investment analysis, risk assessment
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## Installation
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```bash
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# English version
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cp -r skills/research-en ~/.claude/skills/research
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# Chinese version
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cp -r skills/research-zh ~/.claude/skills/research
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# Required: Install agent
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cp agents/web-search-agent.md ~/.claude/agents/
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```
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## Commands
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> **Note**: Use `run /research` instead of `/research` directly, as slash commands conflict with built-in commands.
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| Command | Description |
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|---------|-------------|
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| `run /research` | Generate research outline with items and fields |
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| `run /research/add-items` | Add more items to existing outline |
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| `run /research/add-fields` | Add more fields to existing outline |
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| `run /research/deep` | Deep research each item with parallel agents |
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| `run /research/report` | Generate markdown report from JSON results |
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## Workflow & Example
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> **Example**: Researching "AI Agent Demo 2025"
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### Phase 1: Generate Outline
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```
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run /research AI Agent Demo 2025
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```
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💡 **What happens**: Tell it your topic → It creates a research list for you
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**You get**: A list of 17 AI Agents to research (ChatGPT Agent, Claude Computer Use, Cursor, etc.) + what info to collect for each
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### Phase 2: Deep Research
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```
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run /research/deep
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```
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💡 **What happens**: AI automatically searches the web for each item, one by one
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**You get**: Detailed info for each Agent (company, release date, pricing, tech specs, reviews...)
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### Phase 3: Generate Report
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```
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run /research/report
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```
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💡 **What happens**: All data → One organized report
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**You get**: `report.md` - A complete markdown report with table of contents, ready to read or share
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## Need Help?
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If you have questions, ask Claude Code to explain this project:
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```
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Help me understand this project: https://github.com/Weizhena/deep-research-skills
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```
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## References
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- RhinoInsight: Improving Deep Research through Control Mechanisms for Model Behavior and Context
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## License
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MIT
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