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.
What it is, and how people use it
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. It sits on the Agent Skills shelf of the catalog, maintained by @dstackai.
Why builders keep it installedAgent Skills benefits
A skill packages the exact steps, file conventions and checklists an expert would follow. The model spends its context on your feature instead of figuring out how the tool works.
Commit the skill to your project and every teammate's Claude Code, Codex or Cursor run inherits the same quality bar - including the new teammate on day one.
Tuned instructions cut failed attempts and half-baked output. Fewer retries means fewer tokens burned and noticeably shorter sessions on paid model plans.
Skills are plain markdown and scripts the community versions together. A model upgrade changes behaviour, not your workflow - unlike a bespoke prompt that breaks silently.
Contributorsloading from GitHub…
Languagesestimated from snapshot
- Python 70.0%
- TypeScript 18.0%
- Shell 12.0%