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Auto-claude-code-research-in-sleep

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ARIS ⚔️ (Auto-Research-In-Sleep) — Lightweight Markdown-only skills for autonomous ML research: cross-model review loops, idea discovery, and experiment automation. No framework, no lock-in — works wi

Personal AssistantFreeFree tier
#Python
Inputs: text, code
Type
Open Source
Visit Website GitHub (8.0k stars)

About Auto-claude-code-research-in-sleep

ARIS ⚔️ (Auto-Research-In-Sleep) is a lightweight, markdown-only methodology for autonomous machine learning research. It enables cross-model review loops, idea discovery, and experiment automation without locking users into any specific framework or agent. ARIS works with Claude Code, Codex CLI, Cursor, Trae, Antigravity, GitHub Copilot CLI, OpenClaw, or any LLM agent. The tool includes several specialized components: Anti-Autoresearch, which audits research outputs for 61 integrity signals (46 hack patterns across 8 families plus 13 AI-style impressions and 2 advisory signals) and produces a deterministic, reviewer-ready integrity report; ARIS-Movie-Director, which turns a rough story into a movie told in still frames with scene-by-scene validation; and ARIS-in-AI-Offer, a collection of 23 bilingual ML/LLM/multimodal/generative/Agent interview cheat sheets auto-generated via the /render-html skill. The project is open-source and has garnered over 13,700 stars on GitHub.

Key Features

Markdown-only skills for autonomous ML research
Cross-model review loops for idea discovery and experiment automation
Agent-agnostic: works with Claude Code, Codex CLI, Cursor, Trae, GitHub Copilot CLI, OpenClaw, or any LLM agent
Anti-Autoresearch integrity auditing: detects 61 integrity signals across 8 families
ARIS-Movie-Director: multimodal story-to-stills pipeline with scene validation
ARIS-in-AI-Offer: auto-generated bilingual ML/LLM interview cheat sheets
Built-in ARIS-Monitor for tracking agent windows and full-text transcript search

Pros & Cons

Pros
  • Lightweight and framework-agnostic: no vendor lock-in
  • Integrity checking built-in via Anti-Autoresearch
  • Multimodal capabilities expand beyond text (movie, diagrams)
  • Free and open-source with active community (13.7k stars)
  • Works with a wide range of LLM agents and CLI tools
  • Modular skill-based architecture easy to extend
Cons
  • Requires familiarity with command-line interfaces and LLM agents
  • Best suited for users with ML research background
  • Documentation spread across multiple repos and guides
  • May require multiple LLM API keys for cross-model workflows (costs not included)
  • Steep learning curve for the full methodology

Best For

Autonomous machine learning research with multi-model collaborationIntegrity auditing of AI-generated research outputsMultimodal storytelling and method diagram generationAI interview preparation with curated cheat sheetsCross-model collaborative writing and blog generationExperiment automation and idea discovery in ML workflows

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FAQ

What is ARIS?
ARIS (Auto-Research-In-Sleep) is a lightweight, open-source methodology for autonomous ML research using markdown-only skills. It enables cross-model review loops, idea discovery, and experiment automation without locking into a specific framework.
How does ARIS ensure output integrity?
ARIS includes Anti-Autoresearch, which audits outputs for 61 integrity signals across 8 families (A–H) of patterns, plus AI-style impressions and advisory signals, producing a deterministic, reviewer-ready integrity report.
Is ARIS free to use?
Yes, ARIS is completely free and open-source. You only pay for any LLM API usage if you choose to use commercial models.
Which LLM agents does ARIS work with?
ARIS works with Claude Code, Codex CLI, Cursor, Trae, Antigravity, GitHub Copilot CLI, OpenClaw, or any LLM agent that can execute markdown-based skill workflows.
How do I get started with ARIS?
Clone the GitHub repository, follow the setup guide (SETUP_GUIDE.md), and either use ARIS as a skill-based workflow in your preferred agent or install the standalone ARIS-Code CLI. AI agents should read AGENT_GUIDE.md for structured LLM consumption.