Skill
dstack-prototyping
Use with the dstack skill for model-serving work when the image, serving command, resources,…
Cloud & Infrastructure
Skill
/dstack-presets
Create and manage dstack presets: a toolkit that streamlines model inference optimization with agents, and a portable preset format. Use together with the dstack skill, and only when the user explicitly asks to create a preset or manage existing presets, not for deploying or
Install
$ npx -y skills add dstackai/dstack --skill dstack-presets --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/dstack-presets
Context preview
The summary Claude sees to decide when to auto-load this skill.
Create and manage dstack presets: a toolkit that streamlines model inference optimization with agents, and a portable preset format. Use together with the dstack skill, and only when the user explicitly asks to create a preset or manage existing presets, not for deploying or
SKILL.md
dstack-presets.SKILL.mdname: dstack-presets
description: |
Create and manage dstack presets: a toolkit that streamlines model inference optimization with agents, and a portable preset format. Use together with the dstack skill, and only when the user explicitly asks to create a preset or manage existing presets, not for deploying or serving a model.
dstack Presets
Use `/dstack` for CLI commands, YAML fields, apply behavior, fleets, and other dstack syntax. This skill covers creating and managing presets.
Overview
Presets offer two things: a toolkit that streamlines model inference optimization using agents, and a portable format that deploys the final preset to any cloud, Kubernetes cluster, or bare-metal fleet. A preset holds the serving configuration that produced the result, the benchmark it reached, and the exact hardware it was verified on.
Presets are used for three kinds of work: finding an optimized baseline, optimizing through patching source code, and supporting new hardware.
**When to use this skill:**
- The user explicitly asks to create a preset, or to optimize model inference via a preset
- Managing already created presets: watching sessions, listing, exporting, and deleting them via `dstack preset` commands
**When NOT to use this skill:**
- Deploying or serving a model: use a service instead (see the `dstack` skill)
How to use presets
Follow the [presets documentation](https://dstack.ai/docs/concepts/presets.md).
[Configuration reference](https://dstack.ai/docs/reference/dstack.yml/preset.md) | [CLI reference](https://dstack.ai/docs/reference/cli/dstack/preset.md)
Read more
name: dstack-presets description: | Create and manage dstack presets: a toolkit that streamlines model inference optimization with agents, and a portable preset format. Use together with the dstack skill, and only when the user explicitly asks to create a preset or manage existing presets, not for deploying or serving a model.
dstack Presets
Use `/dstack` for CLI commands, YAML fields, apply behavior, fleets, and other dstack syntax. This skill covers creating and managing presets.
Overview
Presets offer two things: a toolkit that streamlines model inference optimization using agents, and a portable format that deploys the final preset to any cloud, Kubernetes cluster, or bare-metal fleet. A preset holds the serving configuration that produced the result, the benchmark it reached, and the exact hardware it was verified on.
Presets are used for three kinds of work: finding an optimized baseline, optimizing through patching source code, and supporting new hardware.
**When to use this skill:**
- The user explicitly asks to create a preset, or to optimize model inference via a preset
- Managing already created presets: watching sessions, listing, exporting, and deleting them via `dstack preset` commands
**When NOT to use this skill:**
- Deploying or serving a model: use a service instead (see the `dstack` skill)
How to use presets
Follow the [presets documentation](https://dstack.ai/docs/concepts/presets.md).
[Configuration reference](https://dstack.ai/docs/reference/dstack.yml/preset.md) | [CLI reference](https://dstack.ai/docs/reference/cli/dstack/preset.md)
Ships withdstack
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.
Get the whole plugin
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Active
Maintenance
Python
Language
MPL-2.0
License
5h ago
Last commit
4y ago
Created
1d ago
Added
Repo: dstackai/dstack