Aris Autonomous Research

---
name: aris-autonomous-research
description: ARIS (Auto-Research-In-Sleep) — Markdown-only autonomous ML research workflows using cross-model review loops, idea discovery, experiment automation, and paper writing with Claude Code or any LLM agent.
triggers:
  - run autonomous research pipeline
  - set up ARIS research workflow
  - use claude code for ML research
  - automate paper writing with AI
  - cross-model research review loop
  - run experiment automation with ARIS
  - install ARIS skills for claude code
  - generate research ideas while sleeping
---

# ARIS — Autonomous Research In Sleep

> Skill by [ara.so](https://ara.so) — Daily 2026 Skills collection.

ARIS is a **zero-dependency, Markdown-only** autonomous ML research system. Each "skill" is a plain `SKILL.md` file that any LLM agent can read and execute. The system orchestrates **cross-model collaboration**: one model executes (Claude Code / Codex) while another critiques (GPT-5.4 / Gemini / GLM / MiniMax), breaking self-review blind spots without any framework or lock-in.

Core capabilities:
- 🔬 **Idea discovery** from a research direction or existing paper
- 🧪 **Experiment automation** with GPU-ready code generation and W&B tracking
- 📝 **Paper writing** (LaTeX, Beamer slides, A0 poster)
- 🔁 **Cross-model review loops** with score progression
- 📬 **Rebuttal drafting** with safety gates (no fabrication, no overpromise, full coverage)

---

## Installation

### 1. Clone the repository

```bash
git clone https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git
cd Auto-claude-code-research-in-sleep

2. Install skills into Claude Code

Copy the skills directory to your project, or symlink it:

# Option A: copy skills to your project
cp -r skills/ /your/project/.claude/skills/

# Option B: symlink (keeps skills up to date)
ln -s /path/to/Auto-claude-code-research-in-sleep/skills /your/project/.claude/skills

Claude Code auto-discovers SKILL.md files in .claude/skills/**. No registration step needed.

3. Configure the MCP reviewer (cross-model review)

ARIS uses the llm-chat MCP server so the executor model can call a second model for review. Install it:

cd mcp-servers/llm-chat
pip install -r requirements.txt   # or: uv pip install -r requirements.txt

Add to your claude_desktop_config.json (or Claude Code MCP config):

{
  "mcpServers": {
    "llm-chat": {
      "command": "python",
      "args": ["/path/to/Auto-claude-code-research-in-sleep/mcp-servers/llm-chat/server.py"],
      "env": {
        "OPENAI_API_KEY": "$OPENAI_API_KEY",
        "LLM_MODEL": "gpt-4o"
      }
    }
  }
}

For alternative reviewers (Kimi, GLM, MiniMax, DeepSeek) set LLM_BASE_URL and LLM_MODEL to the provider's OpenAI-compatible endpoint. No Claude or OpenAI API required.

4. (Optional) Codex MCP for OpenAI Codex as executor