AREX-Skill

🎬 Demo

A one-minute tour of AREX-Skill: DisCo distills a repository into verified, executable skills, the router narrows 5,000+ skills to the one branch a task needs, and a skill-equipped agent clears a task that stalls without it.

https://github.com/user-attachments/assets/9fa59284-4625-44ef-8b43-01c7b6a05cea

🧭 Table of Contents

• News
• Why AREX-Skill
• Library at a Glance
• Auto-Research Benchmark Results
• How AREX-Skill Is Built
• Quick Start
• Usage Examples
• Documentation
• Contributing
• Acknowledgement
• License
• Citation

📣 News

• 2026-08-27: The library reached 1,000 repositories and 5,000+ skills, with a rebuilt router covering the published repository collection.
• 2026-08-03: AREX-Skill launched with DisCo's Creator and Researcher workflows and the first library release covering more than 170 widely used repositories.

💡 Why AREX-Skill

Research knowledge is abundant, but most of it is still written for humans. Papers explain methods and why they work. Repositories contain working implementations. Blogs and examples record practical tricks and failure modes. An agent still has to search through those sources, decide what applies, piece together a workflow, debug missing steps, and determine whether the result is credible.

AREX-Skill turns that missing operating knowledge into an interface an agent can use. Instead of summarizing a repository, an AREX Skill captures when a capability applies, what to run, how to validate it, and how to recover when an experiment fails.

An AREX Skill is a self-contained, agent-readable unit of operating knowledge. It uses the open Agent Skills format as its portable packaging convention, then adds the operating context an agent needs: when to use a capability, what to run, how to validate it, and how to recover when an experiment fails. Each skill is organized around SKILL.md, with optional references/ and scripts/ resources:

skill/
├── SKILL.md       # scope, routing, workflow, and validation
├── references/    # focused instructions and source provenance
└── scripts/       # executable helpers, diagnostics, and checks

When a repository exposes multiple capabilities, its skills are composed into a repository skill graph. A router first narrows a request to an area, family, repository, and workflow; the agent then loads only the branch it needs. This progressive-disclosure design keeps the initial context focused while preserving access to deeper instructions and helpers. See the library guide and architecture guide for the runtime model and routing details.

How skills enter an autonomous research task

A research agent typically follows this pattern:

1. Start with a concrete research goal.
2. Route the request to the relevant skill graph.
3. Load only the needed SKILL.md branch and follow linked skills as needed.
4. Run the recommended procedures and scripts, using the skill's checks and recovery guidance during execution.
5. Validate the result against the relevant checks and target metric. If the run fails, follow the recovery guidance, iterate, and validate again.

The result is not just an answer: it is an experiment or implementation that can be inspected and reproduced.

📊 Library at a Glance

The AREX-Skill Library covers ML engineering, LLMs, computer vision, data science, scientific computing, model deployment, training infrastructure, robotics, generative media, biomedical AI, and related fields. Browse the repository catalog to explore the complete area and package-family inventory, or inspect the imported skills catalog for upstream repositories and source baselines.

The library's repository-skill router narrows a request before the agent loads the matching graph branch. Repository skills are maintained against upstream changes; source commits, validation steps, and refresh requirements are documented in Refreshing Repo Skills.

📈 Auto-Research Benchmark Results

We keep the agent setup, harness, and execution budget fixed, and change only whether the agent has AREX-distilled skills.

Three takeaways:

• Operating knowledge matters. AREX-Skill adds reusable procedures, checks, and recovery paths without changing the underlying agent workflow.
• The advantage is strongest on difficult tasks. Skills help the agent avoid expensive unguided trial-and-error and recover from near-failure states.
• The budget is spent more productively. A relevant skill graph helps the agent reach a useful region of the solution space earlier and spend more of its budget on experiments and validation.

The complete technical report is forthcoming. Until it is published, the table above is the concise summary of the current evaluation results.

⚗️ How AREX-Skill Is Built

The library is produced by DisCo Creator through a four-stage skill-distillation workflow: scope capabilities from an anchor, ground them in admissible evidence, construct a candidate skill graph, and verify and refine it before publication. The anchor can be a source for task-agnostic distillation or a problem for task-oriented distillation. Supporting evidence, validation checks, and unresolved gaps are retained in the construction record.

See DisCo Workflows for the construction lifecycle, and DisCo Meta Skills for the bundled Creator workflows and portable installation guidance.

🚀 Quick Start

1. Install DisCo

DisCo requires Node.js >=22.19.0. The managed installer can prepare a compatible user-level Node.js runtime when needed.

On macOS, Linux, WSL, or Git Bash, install with the managed curl installer:

curl -fsSL https://github.com/VectorSpaceLab/AREX-Skill/releases/latest/download/install-disco.sh | sh

Alternatively, install the package directly with npm:

npm install -g --ignore-scripts @arex-skill/disco

Configure a model provider on first run with /login, or use an en