jilinchenandClaude Opus 4.5
46529e4b88
- Add user-invocable: true to all SKILL.md files (10 files) - Update README with new command format (/research:deep) - Keep backward compatibility notes for older versions Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
3.0 KiB
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
Installation
# English version
cp -r skills/research-en ~/.claude/skills/research
# Chinese version
cp -r skills/research-zh ~/.claude/skills/research
# Required: Install agent
cp agents/web-search-agent.md ~/.claude/agents/
# Required: Install Python dependency
pip install pyyaml
Commands
Claude Code 2.1.0+: Direct
/researchtrigger is now supported!Older versions: Use
run /researchformat instead.
| Command (2.1.0+) | Command (older) | Description |
|---|---|---|
/research |
run /research |
Generate research outline with items and fields |
/research:add-items |
run /research/add-items |
Add more items to existing outline |
/research:add-fields |
run /research/add-fields |
Add more fields to existing outline |
/research:deep |
run /research/deep |
Deep research each item with parallel agents |
/research:report |
run /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
Older versions:
run /research AI Agent Demo 2025💡 What happens: 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
Phase 2: Deep Research
/research:deep
Older versions:
run /research/deep💡 What happens: 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
Older versions:
run /research/report💡 What happens: 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