Claude 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 serv

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dstackai/dstack 2253 262 forks MPL-2.0 Updated 8d ago

Part of dstackai/dstack — 3 skills

Install

skills CLI npx skills add https://github.com/dstackai/dstack/tree/master/skills/dstack-presets

Claude Code claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install dstackai-dstack@llmmart

Git git clone https://github.com/dstackai/dstack.git

The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole dstackai/dstack collection as a plugin from our marketplace. Git is the plain clone.

Skill manifest

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.

Configuration reference | CLI reference

Files (dstack)

SKILL.md 1.6 KB

---
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)