weizhena/deep-research-skillsGit

Multi-stage deep research

Develop structured research reports with data, analysis and a clear outline.

Before you start

Inputs: Research question, source scope and output requirements

Check the original documentation for supported AI tools, models and runtime dependencies.
Open the repository and follow the author’s installation instructions. Model APIs or external services may require separate accounts.
Try your own material, then review the facts, layout and output files.

Read the original installation guide ↗

License: MIT

Source snapshot: 2026-09-15. README ↗

Original license & copyright

MIT License

Copyright (c) 2026 Lan Zheng

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

Try a task like this

Use Multi-stage deep research with my own source material. Confirm the required inputs and available tools, then produce an editable result.

Deep Research Skill for Claude Code / OpenCode / Codex

English | 中文

If you find this project helpful, please give it a star! :star:

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

A structured research workflow skill for Claude Code, OpenCode, and Codex, 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

git clone https://github.com/Weizhena/deep-research-skills.git
cd deep-research-skills

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-5.4)

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

# Required: Enable web search for current shell
export OPENCODE_ENABLE_EXA=1

# Optional: make it permanent
echo 'export OPENCODE_ENABLE_EXA=1' >> ~/.bashrc
source ~/.bashrc

# 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

Important: In OpenCode, ANY model's websearch requires OPENCODE_ENABLE_EXA=1. A plain export only affects the current shell; writing it to ~/.bashrc makes it persistent. Without it, you only get web fetch, which is weaker for the deep research phase.

Codex

# English version
mkdir -p ~/.codex/skills ~/.codex/agents
cp -r skills/research-codex-en/* ~/.codex/skills/

# Chinese version
mkdir -p ~/.codex/skills ~/.codex/agents
cp -r skills/research-codex-zh/* ~/.codex/skills/

# Required: Install web researcher agent and modules
cp agents-codex/web-researcher.toml ~/.codex/agents/
cp -r agents-codex/web-search-modules ~/.codex/agents/

# Required: Install Python dependency
pip install pyyaml

Add or update ~/.codex/config.toml using either method below:

Option A: Automatic script

cd deep-research-skills
bash scripts/install-codex.sh

Option B: Manual edit

suppress_unstable_features_warning = true

[features]
multi_agent = true
default_mode_request_user_input = true

[agents.web_researcher]
description = "Use this agent when you need to research information on the internet, particularly for debugging issues, finding solutions to technical problems, or gathering comprehensive information from multiple sources. This agent excels at finding relevant discussions. Use when you need creative search strategies, thorough investigation, or compilation of findings from multiple sources."
config_file = "agents/web-researcher.toml"

Commands

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

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

Codex: You can trigger these skills from /skills -> List Skills, or ask naturally, for example Use the research skill to build an outline for AI Agent Demo 2025.

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, OpenCode, or Codex 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