dstack Presets
SkillCloud & infraLets your agent create and manage dstack presets for model inference optimization.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the dstack Presets skill
About this capability
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.
What this skill tells your AI
The instructions your AI receives, as published by dstackai/dstack in skills/dstack-presets/SKILL.md and read by ahel’s review.
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 presetcommands
When NOT to use this skill:
- Deploying or serving a model: use a service instead (see the
dstackskill)
How to use presets
Follow the presets documentation.
Signals
- GitHub stars
- 2k
- Forks
- 261
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
dstack-presets- Source
- github.com/dstackai/dstack