Maintainer of this skill?
Automated security scan — not a certification.
by Weizhena
Structured deep research skill for Claude Code/Open Code/Codex with human-in-the-loop control
# Add to your Claude Code skills
git clone https://github.com/Weizhena/Deep-Research-skillsGuides for using ai agents skills like Deep-Research-skills.
Last scanned: 5/10/2026
{
"issues": [],
"status": "PASSED",
"scannedAt": "2026-05-10T06:33:03.706Z",
"semgrepRan": false,
"npmAuditRan": true,
"pipAuditRan": true
}See how Deep-Research-skills compares with popular alternatives.
Deep-Research-skills is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by Weizhena. Structured deep research skill for Claude Code/Open Code/Codex with human-in-the-loop control. It has 2,417 GitHub stars.
Yes. Deep-Research-skills passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/Weizhena/Deep-Research-skills" and add it to your Claude Code skills directory (see the Installation section above).
Deep-Research-skills is primarily written in Python. It is open-source under Weizhena on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh Deep-Research-skills against similar tools.
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Automated security scan — not a certification.
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.
git clone https://github.com/Weizhena/deep-research-skills.git
cd deep-research-skills
# 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
# 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 plainexportonly affects the current shell; writing it to~/.bashrcmakes it persistent. Without it, you only getweb fetch, which is weaker for the deep research phase.
# 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"
Claude Code 2.1.0+: Direct
/skill-nametrigger is now supported!Older versions: Use
run /skill-nameformat instead.Codex: You can trigger these skills from
/skills->List Skills, or ask naturally, for exampleUse 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 |
Example: Researching "AI Agent Demo 2025"
/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
/research-add-items
/research-add-fields
💡 What will happen: Add more research items or field definitions
/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...)
/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
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
MIT