---
name: "ray-distributed-data-compute"
description: "Scale machine learning and data workloads across clusters with Ray Core and Ray Data."
metadata:
  author: "Ray Project"
  version: "1.0.0"
  runtime_compatibility:
    - "codex"
    - "claude-code"
    - "cursor"
    - "windsurf"
triggers:
  - "run distributed task with ray"
  - "create ray actor"
  - "stream data with ray.data"
permissions:
  fs_read: true
  fs_write: "workspace"
  shell_exec: false
  net_access: false
---

# Ray Core Distributed Tasks & Object Store

Builds distributed AI systems with Ray: @ray.remote functions and Actors, Plasma in-memory shared object store, Ray Data streaming datasets, and cluster auto-scaling on Kubernetes.

## When to Invoke
Invoke this skill whenever you need to:
- run distributed task with ray
- create ray actor
- stream data with ray.data

## Execution SOP & Rules
1. Initialize Ray with ray.init() in scripts or connect to existing cluster with ray.init(address='auto').
2. Define distributed tasks with @ray.remote and invoke with func.remote(*args).
3. Retrieve asynchronous task futures using ray.get(object_refs).
4. Use ray.data.read_parquet() with .map_batches() for high-throughput distributed data pipelines.

## Verification Checklist
- Run local linting, syntax validation, and type-checks before reporting completion.
- Ensure all created or modified files adhere to workspace boundaries and avoid uncommitted secrets.
- Verify tests or build commands exit cleanly with return code 0.