Aris Autonomous Ml Research
This is a complete autonomous ML research system built entirely from Markdown files that any LLM can read and execute. The core idea is smart: one model (Claude Code or Codex) runs your research while a different model (GPT, Gemini, MiniMax) acts as adversarial reviewer to catch blind spots. It handles the full pipeline from paper review to idea generation to experiment automation to LaTeX paper writing, all designed to run overnight. The cross-model review loop is the real trick here, iterating until papers hit "clear accept" scores. Setup requires copying Markdown skills to your Claude Code directory and configuring MCP servers for the reviewer model. It includes rebuttal generation with character limits and safety gates against fabrication, plus bonus tools for slides and posters. Zero dependencies beyond the LLM agents themselves.
- skills.sh installs
- 360
- GitHub stars
- 26
- last updated
- Jun 3, 2026
Install
$ npx -y skills add aradotso/trending-skills --skill aris-autonomous-ml-research --agent claude-codeInstalls into .claude/skills of the current project.
Files
---
name: aris-autonomous-ml-research
description: Autonomous ML research workflows using ARIS (Auto-Research-In-Sleep) — Markdown-only skills for cross-model paper review, idea discovery, experiment automation, and paper writing with Claude Code, Codex, or any LLM agent.
triggers:
- "set up ARIS for autonomous research"
- "run research pipeline while I sleep"
- "automate ML paper writing with Claude Code"
- "cross-model review loop for my paper"
- "use ARIS to find research ideas"
- "run experiment automation with ARIS"
- "set up auto paper review workflow"
- "write rebuttal with ARIS"
---
# ARIS — Auto-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. Every "skill" is a plain `SKILL.md` file that any LLM agent can read and execute. It orchestrates cross-model collaboration — one model executes research (Claude Code, Codex, etc.) while another acts as adversarial reviewer (GPT-5.4, Gemini, GLM, MiniMax, etc.) to break self-play blind spots.
Sponsors
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- Repository
- github.com/aradotso/trending-skills
- Category
- Data Science & ML
- First seen
- Jun 3, 2026
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