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dstackai

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dstack-prototyping

Use with the dstack skill for model-serving work when the image, serving command, resources, backend/fleet choice, or service behavior is not proven. Guides task-first prototyping on real hardware, choosing fleets/backends that can reuse idle instances and caches, checking vLLM/S

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Vue d’ensemble

Use with the dstack skill for model-serving work when the image, serving command, resources, backend/fleet choice, or service behavior is not proven. Guides task-first prototyping on real hardware, choosing fleets/backends that can reuse idle instances and caches, checking vLLM/SGLang sources, and verifying the final dstack service with a model request.

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dstack Prototyping

Use /dstack for CLI commands, YAML fields, apply/attach behavior, service URLs, and other dstack syntax. This skill explains how to use dstack runs while the model-serving configuration is still unknown.

Goal

Find a working dstack service configuration for the requested model.

Before submitting a service, use a task on real hardware to test the serving image, install/runtime assumptions, model download, cache path, command, port, launch flags, resources, env vars, backend/fleet choice, and local model request. Then submit the same configuration as a service and verify the model through the dstack service URL.

Choose Where To Run

Pick the offer whose hardware best fits the goal at hand. Only when several offers fit comparably, choose a VM-based backend, an SSH fleet, or a Kubernetes fleet: they support idle instances and/or instance volumes, so later runs reuse the provisioned/idle instance or instance volumes for caching model weights (and possibly other writes), while container-based backends start clean on every run.

Fetch https://dstack.ai/docs/concepts/backends.md and classify backends from the fetched document, not from memory.

Check Serving Sources

Check serving-framework sources early enough to choose the image, command, launch flags, resources, cache paths, request format, and expected model behavior.

For vLLM and SGLang, use these as credible sources:

  • vLLM recipes and model index: https://recipes.vllm.ai/ and https://recipes.vllm.ai/models.json
  • SGLang docs: https://docs.sglang.io/ (fetch /llms.txt for the page index)
  • SGLang model recipes: https://docs.sglang.io/cookbook/autoregressive/intro
  • Release notes: https://github.com/vllm-project/vllm/releases and https://github.com/sgl-project/sglang/releases
  • Performance-loop methodology (profiling, benchmark contracts): https://www.lmsys.org/blog/2026-07-02-agent-assisted-sglang-development

Use A Task Before Service

Before submitting a service, start a long-lived task:

commands:
  - sleep infinity

or an equivalent idle command.

Submit the task detached, attach or SSH into it when available, and run commands inside the live environment. Test the image, installs, model download and cache path, serving command, port, launch flags, local model request, and expected model behavior.

When starting a long-running command in the background from a non-interactive SSH command, use nohup, redirect stdin from /dev/null, and redirect stdout/stderr to a log file so the SSH command returns while the process keeps running. For example (the command can be any long-running command):

nohup vllm serve ... </dev/null > /tmp/vllm.log 2>&1 &

If the image, hardware choice, or major install path changes, submit another task so the changed setup is tested before service verification.

Do not move to a service after checking only GPU visibility, imports, logs, or a health endpoint. Start the server inside the task and send a request that uses the requested model. For a chat or reasoning model, check the response behavior the endpoint is expected to support, such as reasoning output when that model is supposed to expose it.

Follow /dstack structured status guidance when polling task or service status. After requesting a task or service stop before another submission, wait until that run reaches a terminal status. This allows dstack to reuse its instance or instance volumes when available.

Verify As A Service

Submit the service after the task has verified the configuration: image, command, port, resources, env vars, cache mounts if used, backend/fleet choice, and model request.

Use the service as a duplicate check of the same configuration under dstack service runtime. The model request that worked locally in the task must also work through the dstack service URL.

If service verification fails because the image, install, model download, command, resources, cache, or model behavior needs to change, go back to a task. If the tested serving setup is still right and only the dstack service configuration is wrong, fix the configuration and submit the service again.

Voir la source ↗
Métadonnées du fichier
name: dstack-prototyping
description: |
  Use with the dstack skill for model-serving work when the image, serving command, resources, backend/fleet choice, or service behavior is not proven. Guides task-first prototyping on real hardware, choosing fleets/backends that can reuse idle instances and caches, checking vLLM/SGLang sources, and verifying the final dstack service with a model request.
Voir le texte original
---
name: dstack-prototyping
description: |
  Use with the dstack skill for model-serving work when the image, serving command, resources, backend/fleet choice, or service behavior is not proven. Guides task-first prototyping on real hardware, choosing fleets/backends that can reuse idle instances and caches, checking vLLM/SGLang sources, and verifying the final dstack service with a model request.
---

# dstack Prototyping

Use `/dstack` for CLI commands, YAML fields, apply/attach behavior, service URLs,
and other dstack syntax. This skill explains how to use dstack runs while the
model-serving configuration is still unknown.

## Goal

Find a working dstack service configuration for the requested model.

Before submitting a service, use a task on real hardware to test the serving
image, install/runtime assumptions, model download, cache path, command, port,
launch flags, resources, env vars, backend/fleet choice, and local model
request. Then submit the same configuration as a service and verify the model
through the dstack service URL.

## Choose Where To Run

Pick the offer whose hardware best fits the goal at hand. Only when several offers fit comparably, choose a VM-based backend, an SSH fleet, or a Kubernetes fleet: they support idle instances and/or instance volumes, so later runs reuse the provisioned/idle instance or instance volumes for caching model weights (and possibly other writes), while container-based backends start clean on every run.

Fetch `https://dstack.ai/docs/concepts/backends.md` and classify backends
from the fetched document, not from memory.

## Check Serving Sources

Check serving-framework sources early enough to choose the image, command,
launch flags, resources, cache paths, request format, and expected model
behavior.

For vLLM and SGLang, use these as credible sources:

- vLLM recipes and model index: `https://recipes.vllm.ai/` and
  `https://recipes.vllm.ai/models.json`
- SGLang docs: `https://docs.sglang.io/` (fetch `/llms.txt` for the page
  index)
- SGLang model recipes: `https://docs.sglang.io/cookbook/autoregressive/intro`
- Release notes: `https://github.com/vllm-project/vllm/releases` and
  `https://github.com/sgl-project/sglang/releases`
- Performance-loop methodology (profiling, benchmark contracts):
  `https://www.lmsys.org/blog/2026-07-02-agent-assisted-sglang-development`

## Use A Task Before Service

Before submitting a service, start a long-lived task:

```yaml
commands:
  - sleep infinity
```

or an equivalent idle command.

Submit the task detached, attach or SSH into it when available, and run commands
inside the live environment. Test the image, installs, model download and cache
path, serving command, port, launch flags, local model request, and expected
model behavior.

When starting a long-running command in the background from a non-interactive
SSH command, use `nohup`, redirect stdin from `/dev/null`, and redirect
stdout/stderr to a log file so the SSH command returns while the process keeps
running. For example (the command can be any long-running command):

```shell
nohup vllm serve ... </dev/null > /tmp/vllm.log 2>&1 &
```

If the image, hardware choice, or major install path changes, submit another
task so the changed setup is tested before service verification.

Do not move to a service after checking only GPU visibility, imports, logs, or a
health endpoint. Start the server inside the task and send a request that uses
the requested model. For a chat or reasoning model, check the response behavior
the endpoint is expected to support, such as reasoning output when that model is
supposed to expose it.

Follow `/dstack` structured status guidance when polling task or service status.
After requesting a task or service stop before another submission, wait until
that run reaches a terminal status. This allows dstack to reuse its instance or
instance volumes when available.

## Verify As A Service

Submit the service after the task has verified the configuration: image,
command, port, resources, env vars, cache mounts if used, backend/fleet choice,
and model request.

Use the service as a duplicate check of the same configuration under dstack
service runtime. The model request that worked locally in the task must also work
through the dstack service URL.

If service verification fails because the image, install, model download,
command, resources, cache, or model behavior needs to change, go back to a task.
If the tested serving setup is still right and only the dstack service
configuration is wrong, fix the configuration and submit the service again.

Examiner la source

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MPL-2.0
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Réviser avant installation: Éviter l’installation automatique

Licence: MPL-2.0

  • Dependency or permission surface needs review
  • Permission surface may require sandboxing
  • The skill does not explicitly advise on checking for malicious or untrusted images/modules when prototyping, though it points to official sources which mitigates risk.
  • Quality score needs review
  • Permission surface needs review: secrets or environment access, shell or command execution
  • Dependency/runtime risk: command execution surface, credential or environment access
  • Permission surface: secrets or environment access, shell or command execution
Ouvrir l’audit complet

Les outils sont des indications de métadonnées, pas une compatibilité testée. Les prompts sont des suggestions.

Commencer par une petite tâche

  1. 1Lisez la source et confirmez entrées, résultats, dépendances et permissions.
  2. 2Demandez un plan à l’agent. Approuvez la configuration et les coûts avant un test isolé.
  3. 3Vérifiez résultats et fichiers modifiés. Signalez uniquement ce qui a été exécuté et conservez la révision source.

Vérifiez les dépendances, clés API et frais externes dans la source. Un dépôt public ne rend pas tous les services gratuits.

Source et conseils d’utilisation

Répertorié

Métadonnées et examens sont indicatifs. Popularité, découverte et exécution réussie sont des faits distincts.

Dépôt source
dstackai/dstack
Licence
MPL-2.0
Version
1.0.0
Dernier push GitHub
21 août 2026
Registre mis à jour
1 sept. 2026
Chemin des instructions
skills/dstack-prototyping/SKILL.md

Version déclarée dans le registre ; vérifiez les versions de la source.

Qualité

77/100

Solide

Confiance

63/100

Sandbox uniquement

Audit

77/100

Revue nécessaire

Ouvrir l’audit completVoir le rapport d’évaluation
  • Dependency or permission surface needs review
  • Permission surface may require sandboxing
  • The skill does not explicitly advise on checking for malicious or untrusted images/modules when prototyping, though it points to official sources which mitigates risk.
  • Quality score needs review
  • Permission surface needs review: secrets or environment access, shell or command execution
  • Dependency/runtime risk: command execution surface, credential or environment access
  • Permission surface: secrets or environment access, shell or command execution
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    "audit": "https://www.openagentskill.com/skills/dstackai-dstack-prototyping/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=dstackai-dstack-prototyping&task=Use%20dstack-prototyping%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20dstack-prototyping%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20dstack-prototyping%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/dstackai-dstack-prototyping/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/dstackai-dstack-prototyping"
  }
}

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dstackai
Indexé par
Index communautaire OpenAgentSkill

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