Auto Claude Code Research In Sleep — AI Agent Review & Live Stats

Live GitHub stats, community sentiment, and trend data for Auto Claude Code Research In Sleep. TrendingBots tracks star velocity, fork activity, and what developers are saying — updated from real data sources.

GitHub data synced: Apr 2, 2026 • Sentiment updated: Mar 16, 2026

GitHub Statistics

Community Sentiment

Community Buzz: The project seems to be focused on automating research tasks using AI tools, particularly Claude. It appears to be a niche project with a dedicated community.

Why Auto Claude Code Research In Sleep Stands Out

ARIS is different from alternatives because it provides a radically lightweight, zero-dependency framework for autonomous ML research workflows. By using a modular, skill-based architecture, ARIS enables researchers to easily adapt to different LLM agents and frameworks, and to integrate multiple models into a single workflow. This approach solves the problem of lock-in and allows researchers to focus on their research rather than on learning a new framework. Additionally, ARIS's use of cross-model review loops and experiment automation enables researchers to identify and address weaknesses in their research more effectively.

Built With

Build an autonomous research assistant that reads papers and generates rebuttals — ARIS enables this by providing a lightweight, Markdown-only framework for cross-model review loops and experiment automation, Build a custom research pipeline that clones codebases and generates ideas to fix weaknesses — ARIS allows this through its targeted mode, which takes a research direction and handles everything from paper reading to experiment running, Build a research workflow that integrates with multiple LLM agents, including Claude Code and Codex — ARIS supports this by providing a modular, skill-based architecture that can be easily adapted to different agents and frameworks, Build a paper review and improvement system that uses AI to identify weaknesses and suggest fixes — ARIS enables this by providing a set of pre-built skills for paper review, idea generation, and experiment automation, Build a research automation system that can be used with minimal dependencies and no lock-in — ARIS allows this by providing a plain Markdown file-based system that can be easily forked, rewritten, and adapted to different stacks

Getting Started

  1. Install ARIS by running the command `pip install aris`
  2. Configure ARIS by creating a new Markdown file and adding the necessary skills and parameters
  3. Run ARIS in basic mode by using the command `/research-pipeline 'factorized gap in discrete diffusion LMs'`
  4. Configure ARIS to use a specific LLM agent, such as Claude Code or Codex, by adding the necessary parameters to the Markdown file
  5. Try running ARIS in targeted mode by using the command `/research-pipeline 'improve method X' — ref paper: https://arxiv.org/abs/2406.04329, base repo: https://github.com/org/project` to verify that it works

About

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 with Claude Code, Codex, OpenClaw, or any LLM agent.

Category & Tags

Category: research

Tags: ai-research, ai-tools, aris, autonomous-agent, claude, claude-code, claude-code-skills, codex, deep-learning, gpt, idea-generation, llm, machine-learning, mcp, mcp-server, ml-research, openai, paper-review, paper-writing, research-automation

Market Context

This project is part of a growing trend of AI-powered research automation tools, which are gaining popularity in academic and professional settings.