Deep Research Skill for Claude Code

Inspired by RhinoInsight: Improving Deep Research through Control Mechanisms for Model Behavior and Context

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.

Use Cases

• Academic Research: Paper surveys, benchmark reviews, literature analysis
• Technical Research: Technology comparison, framework evaluation, tool selection
• Market Research: Competitor analysis, industry trends, product comparison
• Due Diligence: Company research, investment analysis, risk assessment

Commands

Command	Description
/research	Generate research outline with items and fields
/research/add-items	Add more items to existing outline
/research/add-fields	Add more fields to existing outline
/research/deep	Deep research each item with parallel agents
/research/report	Generate markdown report from JSON results

Installation

Copy the skills/ folder to your Claude Code directory:

cp -r skills/* ~/.claude/skills/

Workflow

Phase 1: Generate Outline

/research <topic>

• Uses model knowledge + web search
• Asks for existing field definitions
• Creates {topic}/ directory with separated files

Phase 2: Deep Research

/research/deep

• Reads outline and fields automatically from current directory
• Launches parallel agents (5 per batch)
• Agents read fields.yaml independently (not passed in prompt)
• Outputs structured JSON per item
• Supports resume from checkpoint

Optional: Expand Outline

/research/add-items    # Add more research targets
/research/add-fields   # Add more field definitions

Output Format

Directory Structure

{topic}/
  ├── outline.yaml    # items + execution config
  ├── fields.yaml     # field definitions
  └── results/        # deep research outputs

outline.yaml

topic: "your topic"
items:
  - name: "Item1"
    source: "source info"
execution:
  batch_size: 5          # parallel agents (default: 5)
  items_per_agent: 1     # items per agent (default: 1)
  output_dir: "./results" # output directory (default: ./results)

fields.yaml

basic_info:
  - name: "field_name"
    description: "field description"
    detail_level: "detailed|brief"

Research Result (JSON)

Each item outputs a structured JSON file with all defined fields.

Phase 3: Generate Report

/research/report

• Generates Python script to convert JSON to markdown
• Creates report with table of contents and anchor links
• Skips uncertain fields automatically

References

• RhinoInsight: Improving Deep Research through Control Mechanisms for Model Behavior and Context

License

MIT