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

Active·★ 14.2k·MIT·Updated 2026-08-04
★ Trending★ Multi-Agent★ Workflow Automation

Auto-claude-code-research-in-sleep (ARIS) is a set of custom Claude Code skills for autonomous ML research workflows. It orchestrates cross-model collaboration, with Claude Code executing research tasks and an external LLM (like GPT-5.4) critically reviewing. This system can autonomously discover ideas, run experiments, and write/refine research papers, allowing researchers to wake up to ready-to-submit results.

Auto-claude-code-research-in-sleep is currently grouped under Workflow Automation, which makes it easier to evaluate through workflow fit instead of isolated features alone. Based on the available data, it leans most heavily toward 18 composable skills for flexible workflow chaining. and Explore new research areas and discover novel ideas through literature surveys and brainstorming.. The listed license is MIT, which is useful when adoption constraints matter. It also shows measurable community traction with 14.2k GitHub stars.

#ai-research#ai-tools#aris#autonomous-agent#claude#claude-code#claude-code-skills#codex
$ Install
$ git clone https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git && cp -r Auto-claude-code-research-in-sleep/skills/* ~/.claude/skills/ && npm install -g @openai/codex && claude mcp add codex -s user -- codex mcp-server
↗ Visit site★ GitHub
01

Features

0118 composable skills for flexible workflow chaining.
02Automated idea discovery including literature survey, brainstorming, novelty check, and GPU pilot experiments.
03Autonomous multi-round review loop to iteratively improve research with experiments.
04Comprehensive paper writing pipeline from narrative to submission-ready LaTeX/PDF.
05Cross-model collaboration for adversarial review, breaking single-model blind spots.
02

Why choose it

+18 composable skills for flexible workflow chaining.
+Explore new research areas and discover novel ideas through literature surveys and brainstorming.
+Covers 10 supported environments or platforms, which is helpful for broader deployment needs.
+Ships with a public repository and a MIT license, which makes adoption and review easier.
03

Trade-offs

!There are at least 8 related tools in the same category, so the best choice is easier to make after side-by-side comparison.
04

Compatibility

Claude Code
Core Executor
Verified via docs
Codex CLI
Critical Reviewer
Verified via docs
LaTeX
Paper Compilation
Verified via docs
GLM
Alternative Executor
Verified via docs
MiniMax
Alternative Reviewer
Verified via docs
Zotero
Literature Search
Verified via docs
05

Quick start

1
$ git clone https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git
2
$ cp -r Auto-claude-code-research-in-sleep/skills/* ~/.claude/skills/
3
$ npm install -g @openai/codex
4
$ claude mcp add codex -s user -- codex mcp-server
06

Use cases

↳Explore new research areas and discover novel ideas through literature surveys and brainstorming.
↳Automate the iterative review and refinement of research projects, including running experiments, until submission-ready.
↳Transform research narratives into submission-ready academic papers with a full writing and formatting pipeline.
07

How it compares

≈Auto-claude-code-research-in-sleep sits in the Workflow Automation category, so it makes more sense to evaluate it alongside tools like Gemini CLI instead of in isolation.
≈If your main need is closer to "Explore new research areas and discover novel ideas through literature surveys and brainstorming.", that use case is a better lens for comparison than broad feature checklists alone.
≈Auto-claude-code-research-in-sleep uses a MIT license, and community traction are both easier to judge in category context.
08

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Related searches

Auto-claude-code-research-in-sleep AlternativesBest Workflow Automation Tools 2026Open Source Workflow AutomationAuto-claude-code-research-in-sleep TutorialAuto-claude-code-research-in-sleep Vs Competitorsai-researchai-toolsaris

Comments

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  • ?
    usr_seed_0816Apr 20, 2026

    Claude Code skills for autonomous research workflows — the Codex MCP integration is well-implemented.

  • ?
    usr_seed_0619Apr 6, 2026

    Idea discovery automation for ML research is genuinely novel. The cross-model approach surfaces things a single model misses.

  • ?
    usr_seed_0813Apr 3, 2026

    Autonomous ML research while you sleep is the correct use of async agent capabilities.

  • ?
    usr_seed_0936Mar 22, 2026

    Cross-model review loops via Codex MCP is a clever way to get multiple AI perspectives automatically.

On this page
01Features02Why choose it03Trade-offs04Compatibility05Quick start06Use cases07How it compares08Alternatives
Stats
GitHub Stars★ 14.2k
Last commit1d ago
StatusActive
LicenseMIT
CategoryWorkflow Automation
Trend (30d)
+0.5k↑ 3.8%
Links
Documentation↗Discussion↗Issues↗Releases↗

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