Overview
ARIS (Auto-Research-In-Sleep) is a set of plain-Markdown skills that turn an LLM coding agent into an autonomous research assistant. Instead of a framework or runtime, it ships opinionated workflows for cross-model paper review, research idea generation, and experiment automation. The skills are model-agnostic and drop into Claude Code, Codex, OpenClaw, or any agent that reads Markdown instructions.
Key Features
- Cross-model review loops where multiple LLMs critique and refine each other's outputs
- Research idea discovery and paper-review workflows for ML literature
- Experiment automation that runs and iterates on ML jobs hands-off
- Pure Markdown skills with no framework lock-in or extra dependencies
- Compatible with Claude Code, Codex, OpenClaw, and general LLM agents
Use Cases
- Running overnight autonomous literature reviews and paper critiques
- Generating and stress-testing novel research ideas across models
- Automating repetitive ML experiment and ablation pipelines
- Building a self-improving research loop inside an existing coding agent
Technical Details
- Markdown-only skill definitions — no daemon, runtime, or vendor SDK required
- Designed around cross-model orchestration and iterative agent loops
- Integrates via standard skill/tool-loading mechanisms in agents like Claude Code