---

name: cluster-ops description: "Operate Ray clusters, jobs, dashboard access, runtime environments, state APIs, logs, and observability workflows safely from the Ray CLI." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Ray Cluster Operations

Use this sub-skill when the task is about administering or inspecting Ray clusters and submitted Ray applications: ray start, ray stop, ray status, ray job submit, ray job logs, ray job status, RAY_API_SERVER_ADDRESS, dashboard forwarding, runtime environments, ray list/get/summary/logs, cluster YAML, VM/KubeRay connection concepts, or OOM debugging.

Route First

• For Ray Core application code, task/actor APIs, ray.init(), object refs, scheduling options, or placement groups in Python, route to ../core-runtime/SKILL.md.
• For Ray Serve deploy/run/status/config details, Serve application YAML, HTTP ingress, or ray serve, route to ../serve-deployments/SKILL.md.
• For Ray Data, Train, Tune, or RLlib workload internals, route to the matching sibling sub-skill after using this sub-skill only for cluster/job visibility.
• For cloud account, Kubernetes credential, network policy, or VM provisioning, keep guidance high-level and ask the user for their platform-specific constraints before issuing mutating commands.

Operating Rules

1. Prefer read-only inspection first: ray --help, ray status --help, ray job --help, ray list --help, ray summary --help, and this sub-skill's scripts/ray_cli_doctor.py.
2. Distinguish local bootstrap addresses from dashboard HTTP addresses. Jobs and State API commands usually need the dashboard/API server address such as http://127.0.0.1:8265; Ray Core drivers may use a Ray bootstrap address or Ray Client URI.
3. Treat ray start, ray stop, ray up, ray down, ray exec, ray submit, ray attach, ray dashboard, and ray symmetric-run as mutating or long-running unless the user explicitly authorizes them.
4. Use ray summary before broad ray list calls on busy clusters; state snapshots can be stale, partial, or truncated.
5. For jobs, capture the submission ID, dashboard address, runtime environment, working directory, and exact entrypoint before debugging status or logs.

References

• references/cli-reference.md maps the Ray CLI, read-only versus mutating commands, state/log patterns, and dashboard/status workflows.
• references/job-and-runtime-env.md covers Jobs CLI/API use, RAY_API_SERVER_ADDRESS, --working-dir, runtime environment packaging, and remote/KubeRay dashboard access concepts.
• references/troubleshooting.md covers connection failures, missing extras, dashboard/state API issues, runtime-env upload/import failures, OOM/system exits, and unsafe command handling.
• scripts/ray_cli_doctor.py checks local Ray CLI availability and selected command help without starting, stopping, or connecting to a cluster.

Quick Triage

python scripts/ray_cli_doctor.py
python scripts/ray_cli_doctor.py --command job
python scripts/ray_cli_doctor.py --command list

If a user asks you to run a mutating cluster command, first restate what it changes, confirm the target address or cluster config, and prefer a dry-run/help command when available.