AREX-Skill
A library of thousands of ML skills distilled from popular repositories, plus the DisCo CLI to load them into an agent
- 351 ★ on the repository
- Last commit
- Apache-2.0
Extended access
Why this level
- Skills run scripts and experiments on the machine, install dependencies and download heavy models and datasets
- The managed installer runs as a script fetched with curl, and DisCo stores a model provider key
Install
- Works in
Manual install
curl -fsSL https://github.com/VectorSpaceLab/AREX-Skill/releases/latest/download/install-disco.sh | shManaged installer for macOS, Linux, WSL and Git Bash; it can prepare a compatible Node.js.
Access and checks
How we reviewExtended accessThe tool runs code or makes network calls and only reads external data.
- Skills run scripts and experiments on the machine, install dependencies and download heavy models and datasets
- The managed installer runs as a script fetched with curl, and DisCo stores a model provider key
This is third-party code. Review the repository files before installing.
What it does
AREX-Skill turns the documentation and code of popular ML repositories into skills that an agent runs step by step. Each skill says when a capability applies, what to run, how to verify the result and how to recover when an experiment fails. The format follows the open Agent Skills standard around a SKILL.md file, with references and scripts folders beside it. The library covers more than 1000 repositories and more than 5000 skills across 20 areas, from model serving and LLM fine-tuning to computer vision, robotics and biomedicine. The working tool is the DisCo CLI from the @arex-skill/disco package. The disco repo-skills install command installs the library, and Researcher mode picks the skill branch for a task through a router: the agent loads only the section it needs, not the whole library. Creator mode exports selected skills to other agents. Importing the vllm and sglang skills into ~/.claude, for example, makes them available in Claude Code, and ~/.agents is picked up by Codex.
Who it is for. ML engineers and researchers who ask Codex or Claude Code to set up inference, fine-tune a model or reproduce an experiment and want the agent to follow verified steps.
Good fit when
- You want to compare vLLM and SGLang on one model and hardware and keep the commands and measurements for a rerun
- An agent runs AlphaFold2 or another heavy pipeline where one skipped step breaks the experiment
- You need skills for only one or two repositories from the library and want to export them to Claude Code or Codex
- The task is a long research one: the router narrows the choice to one repository and workflow so the context stays small
- You build skills for your own repository from its code and paper through Creator mode
Not a fit when
- The task is far from ML: the library holds scientific and ML packages and no general development skills
- The machine has no Node.js 22.19 or newer and cannot get it, and DisCo does not run without it
- You need one small skill for one command: copying the right SKILL.md is enough, without the whole CLI
- You expect reproducible results on weak hardware: skills describe procedures but do not replace GPUs and memory
Example request
Use the installed skills to benchmark vLLM and SGLang on this model and this machine and keep the commands needed to reproduce the comparisonLimitations
- DisCo needs Node.js 22.19 or newer. The managed installer can prepare a suitable Node.js in a user directory.
- Set a model provider with /login on first run or with OPENAI_API_KEY, ANTHROPIC_API_KEY or GEMINI_API_KEY.
- Skills run scripts and experiments, so the result depends on the GPU, memory and package versions on the machine. Outputs of an untrained model are not scientific predictions.
- The library is large and reflects ML repositories as of 2026-08-27. Skills are tied to source commits and updated through a separate refresh procedure.
How to disable. Delete imported skills from ~/.claude/skills or ~/.agents/skills and restart the agent. npm uninstall -g @arex-skill/disco removes DisCo itself.
FAQ
How do I install AREX-Skill skills in Claude Code?
Install DisCo, run disco repo-skills install and export the skills you want with disco --creator -p and an import request to ~/.claude. Then restart Claude Code so it rereads the skills directory. For Codex, name ~/.agents in the request.
What is the router in AREX-Skill?
The router narrows a request to an area, family, repository and workflow, and the agent loads only the matching SKILL.md branch. The initial context stays small and deeper instructions load as the work proceeds.
Does the library work without DisCo?
Yes, skills sit in the skills folder as ordinary directories with SKILL.md, references and scripts, and you can copy them into an agent's skills directory by hand. DisCo adds the router, Researcher and Creator modes and export of several repositories at once.
How is AREX-Skill different from an ordinary skill pack?
The skills are distilled from repositories and papers and include verification steps and recovery after a failed run. For complex repositories a graph links them, so one repository yields several related skills.
Which Node.js version does DisCo need?
Node.js 22.19 or newer. The managed installer can prepare a suitable version in a user directory, while the npm route needs it already installed. Set the model provider with /login or the OPENAI_API_KEY, ANTHROPIC_API_KEY or GEMINI_API_KEY variable.
Discussion
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Comparison with similar tools
Cards that share the main search query, and how they differ.
| Tool | Official | Authentication | Repository stars | Main difference |
|---|---|---|---|---|
| AREX-Skill (this card) | no | 351 | ||
| Awesome Autoresearch | no | 2,591 | a link collection of autonomous research loops rather than executable skills | |
| Skill Seekers | no | not required | 15,301 | converts docs and repositories into skills on your side instead of shipping a ready library |
README in short
The README explains the idea of operating knowledge for agents and shows a skill's structure of SKILL.md, references and scripts. It then covers the library's data, results on four autonomous research benchmarks and how it is built. The quick start includes installing DisCo, running Researcher mode, importing skills into other agents and links to docs on creating and refreshing skills.