npx skills add majiayu000/claude-skill-registry --skill ray --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Ray Ray is the compute layer for AI. It powers ChatGPT training and massive scale workloads. v3.0 (2025) improves **efficiency** and adds an **MCP Server** for agents. ## When to Use - **Distributed Training**: Scaling PyTorch across 100 GPUs. - **Ray Serve**: Serving LLMs with high throughput (vLLM integration). - **Hyperparameter Tuning**: Ray Tune is the industry standard. ## Core Concepts ### Actors & Tasks - **Task**: Stateless function (like Lambda). - **Actor**: Stateful class (like a microservice). ### Object Store Shared memory across the cluster means zero-copy data sharing. ## Best Practices (2025) **Do**: - **Use `ray.data`**: For streaming massive datasets into trainers. - **Use KubeRay**: The Kubernetes operator for managing Ray clusters. - **Use Ray Serve**: It supports "Model Composition" (chaining models). **Don't**: - **Don't use for simple scripts**: The overhead of starting a Ray cluster is 5-10s. ## References - [Ray Documentation](https://docs.ray.io/)
- When to Use
- Core Concepts
- Actors & Tasks
- Object Store
- Best Practices (2025)
- References
What does the ray skill do?
Ray distributed computing framework. Use for scaling ML.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ray --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.