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

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
Outputs {topic}_outline.yaml in current directory

Phase 2: Deep Research

/research/deep

Reads outline automatically from current directory
Launches parallel agents (5 per batch)
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

Outline (YAML)

topic: "your topic"
items:
  - name: "Item1"
    source: "source info"
fields:
  basic_info:
    - name: "field_name"
      description: "field description"
      detail_level: "detailed|brief"
execution:
  batch_size: 5

Research Result (JSON)

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

References

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

License

MIT

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