dstack-presets
Create and manage dstack presets: a toolkit that streamlines model inference optimization…
dstack is an open-source control plane for GPU provisioning and orchestration across GPU clouds, Kubernetes, and on-prem clusters.
$ npx -y skills add dstackai/dstack --skill dstack --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/dstackContext preview
The summary Claude sees to decide when to auto-load this skill.
dstack is an open-source control plane for GPU provisioning and orchestration across GPU clouds, Kubernetes, and on-prem clusters.
name: dstack description: | dstack is an open-source control plane for GPU provisioning and orchestration across GPU clouds, Kubernetes, and on-prem clusters.
`dstack` provisions and orchestrates workloads across GPU clouds, Kubernetes, and on-prem via fleets.
**When to use this skill:**
`dstack` operates through three core components:
1. `dstack` server - Can run locally, remotely, or via dstack Sky (managed) 2. `dstack` CLI - Applies configurations and manages or inspects fleets, runs, logs, events, volumes, gateways, and offers; it uses project configurations stored in `~/.dstack/config.yml`, which can be managed with `dstack project` 3. `dstack` configuration files - YAML files ending with `.dstack.yml`
`dstack apply` shows a plan and submits configuration changes. For run configurations, it attaches when the run reaches `running` by default: it configures SSH access, forwards declared ports, and streams logs. With `-d`, it submits and exits.
1) Show plan: `echo "n" | dstack apply -f <config>` 2) If plan is OK and user confirms, apply detached: `dstack apply -f <config> -y -d` 3) Check the run: `dstack run get <run-name> --json` 4) If dev-environment or task with ports and running: attach to surface IDE link/ports/SSH alias (agent runs attach in background); ask to open link 5) If attach fails in sandbox: request escalation; if not approved, ask the user to run `dstack attach` locally and share the output
**CRITICAL: Never propose `dstack` CLI commands or YAML syntaxes that don't exist.**
**NEVER do the following:**
dstack --help # List all commands dstack apply -h <configuration type> # Flags for apply per configuration type (dev-environment, task, service, fleet, etc) dstack fleet --help # Fleet subcommands dstack ps --help # Flags for ps
**Commands that stream indefinitely in the foreground:**
Agents should avoid blocking: use `-d`, timeouts, or background attach. When attach is needed, run it in the background by default (`nohup ...`), but describe it to the user simply as "attach" unless they ask for a live foreground session.
When waiting programmatically for a specific run, use `dstack run get <run-name> --json` and read its top-level `status`. Run statuses are `pending`, `submitted`, `provisioning`, `running`, `terminating`, `terminated`, `failed`, and `done`; the last three are terminal. Stop waiting when the run reaches the state needed for the next action or a terminal status. Never parse or grep human-readable `dstack ps` output; its status column may display a job message such as `no offers`.
**All other commands:** Use 10-60s timeout. Most complete within this range. **While waiting, monitor the output** - it may contain errors, warnings, or prompts requiring attention.
**Confirmation handling:**
**Best practices:**
After submitting a run with `-d` (dev-environment, task, service), first determine whether submission failed. If the apply output shows errors (validation, no offers, etc.), stop and surface the error.
If the run was submitted, check it with `dstack run get <run-name> --json`, then guide the user through relevant next steps: If you need to prompt for next actions, be explicit about the dstack step and command (avoid vague questions). When speaking to the user, refer to the action as "attach" (not "background attach").
A unified orchestration layer for heterogeneous AI compute. It standardizes how to manage compute and run training and inference on GPU clouds, Kubernetes, VMs, or bare-metal clusters.
Repo: dstackai/dstack
Create and manage dstack presets: a toolkit that streamlines model inference optimization…
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