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ARIS — Auto Research in Sleep

Skill Data & Analytics Usable
Works inClaude Code
Usable Reviewed

Autonomous ML research loops that run cross-model review, idea discovery, and experiment automation while you sleep. Designed for overnight batch research.

ARIS (Auto Research in Sleep) is a skill pack for Claude Code that runs autonomous ML research pipelines — literature review, idea generation, experiment execution, and paper writing — with cross-model review, where a second model (e.g. GPT via the Codex MCP) audits Claude's work. Installed by cloning the repo and symlinking its skills into your project; adaptations exist for Codex CLI, Cursor, and other agent hosts.

13,571 starsMIT (commercial OK)FreeQuick setup
Usable rating — This tool is functional but has notable gaps. Review the evaluation notes below before deploying.

Autonomous ML research loops that run cross-model review, idea discovery, and experiment automation while you sleep. Designed for overnight batch research. ~1.2K stars.

ML researchers and data scientists who want to queue up research tasks for Claude to execute autonomously during off-hours.

Claude Code Claude Cowork Claude Chat

https://github.com/wanshuiyin/Auto-claude-code-research-in-s...

By wanshuiyin

How to Get It

Option 1: Claude Desktop App (Code Mode)Click the + button next to the prompt box → PluginsAdd plugin. Search and click Install. Skills work in Claude Code only.
Option 2: Paste into Claude CodeCopy the command below and paste it into your conversation. Claude will install it.
Instructions to paste into Claude
git clone https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git
bash Auto-claude-code-research-in-sleep/tools/install_aris.sh ~/your-project
First thing to try

After installing, paste this into Claude:

Help me queue up research experiments to run overnight while me sleep
PrerequisitesClaude Code and git; optional Codex CLI/MCP for cross-model review (default reviewer model is GPT routed via Codex)CostFree

Trust Signals Reviewed

Stars13,571Contributors82Last updated2026-07-14LicenseMIT (OK for commercial use)Known CVEsNone foundSources: GitHub Advisory Database + OSV.dev · Scanned 2026-07-19 · scanner v1

Community Pulse Active

Discussed on Reddit

3 mentions across 1 sources

Reviewer notes

Reviewed review. These are observations, not a security certification.

Niche ML research skills. Star count may be inflated vs actual adoption. Legitimate Claude Code skills.

Things to check

  • Requires careful prompt engineering and monitoring to avoid divergent experiment directions; token costs scale with experiment depth and may accumulate silently in background runs. No built-in safety guardrails for resource-intensive loops.

How to evaluate tools before deploying →

Data shown here comes from public APIs and automated scanning. Reviewer notes reflect one person's experience. This is not a security certification or legal recommendation. Always evaluate tools according to your own organization's policies.

Evaluation

Ease of Use
3/5
Versatility
2/5
Reliability
3/5
Security
3/5
Overall score2.75 / 5.00 UsableEvaluatedApr 2026
Niche ML research skills. Star count may be inflated vs actual adoption. Legitimate Claude Code skills.

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