jilinchenandClaude Opus 4.5
a34ef20fd4
- New report command to generate markdown from JSON results - Generates Python script for conversion - Creates report with TOC and anchor links - Skips uncertain fields and uncertain array - Update deep.md with prompt template format 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
106 lines
2.9 KiB
Markdown
106 lines
2.9 KiB
Markdown
# Deep Research Skill for Claude Code
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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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## Commands
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| Command | Description |
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|---------|-------------|
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| `/research` | Generate research outline with items and fields |
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| `/research/add-items` | Add more items to existing outline |
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| `/research/add-fields` | Add more fields to existing outline |
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| `/research/deep` | Deep research each item with parallel agents |
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| `/research/report` | Generate markdown report from JSON results |
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## Installation
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Copy the `skills/` folder to your Claude Code directory:
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```bash
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cp -r skills/* ~/.claude/skills/
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```
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## Workflow
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### Phase 1: Generate Outline
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```
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/research <topic>
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```
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- Uses model knowledge + web search
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- Asks for existing field definitions
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- Creates `{topic}/` directory with separated files
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### Phase 2: Deep Research
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```
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/research/deep
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```
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- Reads outline and fields automatically from current directory
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- Launches parallel agents (5 per batch)
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- Agents read fields.yaml independently (not passed in prompt)
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- Outputs structured JSON per item
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- Supports resume from checkpoint
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### Optional: Expand Outline
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```
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/research/add-items # Add more research targets
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/research/add-fields # Add more field definitions
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```
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## Output Format
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### Directory Structure
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```
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{topic}/
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├── outline.yaml # items + execution config
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├── fields.yaml # field definitions
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└── results/ # deep research outputs
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```
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### outline.yaml
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```yaml
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topic: "your topic"
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items:
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- name: "Item1"
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source: "source info"
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execution:
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batch_size: 5 # parallel agents (default: 5)
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items_per_agent: 1 # items per agent (default: 1)
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output_dir: "./results" # output directory (default: ./results)
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```
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### fields.yaml
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```yaml
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basic_info:
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- name: "field_name"
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description: "field description"
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detail_level: "detailed|brief"
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```
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### Research Result (JSON)
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Each item outputs a structured JSON file with all defined fields.
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### Phase 3: Generate Report
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```
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/research/report
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```
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- Generates Python script to convert JSON to markdown
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- Creates report with table of contents and anchor links
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- Skips uncertain fields automatically
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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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