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

Installation

Claude Code

# English version
cp -r skills/research-en/* ~/.claude/skills/

# Chinese version
cp -r skills/research-zh/* ~/.claude/skills/

# Required: Install agent and modules
cp agents/web-search-agent.md ~/.claude/agents/
cp -r agents/web-search-modules ~/.claude/agents/

# Required: Install Python dependency
pip install pyyaml

OpenCode (default: gpt-5.2)

# Skills (same as Claude Code)
cp -r skills/research-en/* ~/.claude/skills/   # or research-zh for Chinese

# Required: Install agent and modules
cp agents/web-search-opencode.md ~/.config/opencode/agents/web-search.md
cp -r agents/web-search-modules ~/.config/opencode/agents/

# Required: Install Python dependency
pip install pyyaml

Commands

Claude Code 2.1.0+: Direct /skill-name trigger is now supported!

Older versions: Use run /skill-name format instead.

Command (2.1.0+)	Description
`/research`	Generate research outline with items and fields
`/research-add-items`	Add more research items to existing outline
`/research-add-fields`	Add more field definitions to existing outline
`/research-deep`	Deep research each item with parallel agents
`/research-report`	Generate markdown report from JSON results

Workflow & Example

Example: Researching "AI Agent Demo 2025"

Phase 1: Generate Outline

/research AI Agent Demo 2025

💡 What will happen: Tell it your topic → It creates a research list for you

You get: A list of 17 AI Agents to research (ChatGPT Agent, Claude Computer Use, Cursor, etc.) + what info to collect for each

(Optional) Not satisfied? Add more

/research-add-items
/research-add-fields

💡 What will happen: Add more research items or field definitions

Phase 2: Deep Research

/research-deep

💡 What will happen: AI automatically searches the web for each item, one by one

You get: Detailed info for each Agent (company, release date, pricing, tech specs, reviews...)

Phase 3: Generate Report

/research-report

💡 What will happen: All data → One organized report

You get: report.md - A complete markdown report with table of contents, ready to read or share

Need Help?

If you have questions, ask Claude Code to explain this project:

Help me understand this project: https://github.com/Weizhena/deep-research-skills

References

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

License

MIT