ARIS (Auto-Research-In-Sleep)
Markdown-based skill workflows for autonomous ML research with cross-model review loops, idea discovery, experiment automation and paper writing.
At a Glance
ARIS core skills and tooling released under the MIT License; free to use, copy, modify and distribute. Model/API usage (e.g., Claude, Codex) is an external cost.
Engagement
Available On
Listed Oct 2026
About ARIS (Auto-Research-In-Sleep)
ARIS (Auto-Research-In-Sleep) is an open-source collection of Markdown skills, published by wanshuiyin on GitHub under the MIT license, that lets coding agents carry out machine learning research workflows. A user gives it a research direction, and the skills handle idea discovery, experiments, paper review and writing. The README lists the latest release as v0.4.28, which belongs to the standalone ARIS-Code CLI.
What It Is
ARIS is a methodology delivered as plain SKILL.md files rather than a framework or platform. An executor agent such as Claude Code drives the work, and an independent model (Codex MCP by default) reviews it. The README argues that using two different models avoids the blind spots of a single model reviewing its own output. Skills can be installed into Claude Code, Codex CLI, Cursor, Trae, Antigravity, GitHub Copilot CLI, OpenClaw and other agents. ARIS also ships as a standalone CLI (ARIS-Code) and as Claude Code and Codex CLI plugins.
Workflows and Outputs
The main entry point is /research-pipeline, which takes a direction, or a reference paper plus a base repository, and moves through ideas, experiments and a written paper. Each workflow can also be run on its own. Documented examples include:
/research-implement-feature, which builds a feature one step at a time, each with a runnable check and an assumption ledger./rebuttal, which turns reviews into a grounded rebuttal with gates against fabrication, overpromising and missed concerns, producing a paste-ready text file and an extended draft./paper-slidesand/paper-poster-html, which produce Beamer/PPTX slides with speaker notes and a print-ready HTML poster.- Skills for citation verification, a research wiki, proof checking, and Lean formalization.
Reviewer and Model Options
The default reviewer is Codex with a GPT-6-Astra model, and the README documents fallback to earlier models when access is missing. Other reviewer routes include Oracle MCP for GPT-5.5 Pro, Gemini via the Antigravity CLI, and a manual-review MCP that lets you paste prompts into any non-Claude model. The README also describes alternative model combinations using GLM, MiniMax, Kimi and ModelScope-hosted models.
Setup Path
The README describes a three-command quick start, with a re-registration step for the codex MCP server after codex-cli 0.154 removed codex mcp-server. ARIS provides its own codex exec bridge for this. Installers support selective skill installation and updates.
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Pricing
Open Source (MIT)
ARIS core skills and tooling released under the MIT License; free to use, copy, modify and distribute. Model/API usage (e.g., Claude, Codex) is an external cost.
- MIT License
- Markdown-only skills for autonomous ML research
- Cross-model review loops, idea discovery, experiment automation
- Works with Claude Code, Codex CLI, Cursor, OpenClaw and other LLM agents
- Standalone ARIS-Code CLI and plugins
Capabilities
Key Features
- Cross-model review loop with executor and independent reviewer
- End-to-end research pipeline from direction to paper
- Targeted mode using a reference paper and base repository
- Feature implementation with assumption ledger and runnable checks
- Rebuttal drafting with no-fabrication, no-overpromise and full-coverage gates
- Slides and HTML poster generation
- Citation verification against arXiv, CrossRef and Semantic Scholar
- Persistent research wiki
- Proof checking and Lean formalization
- Selective skill installation and smart updates
- Standalone ARIS-Code CLI
- Claude Code and Codex CLI plugins