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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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価格未確認★ 2,216 GitHub スター登録情報の更新日 · 2026年9月1日agent-skill

概要

説明全文を読む

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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.

ソースを見る ↗

ファイルのメタデータ

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.

元のテキストを表示

---
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

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.

ソースを確認

価格と実行コスト

Skill の入手: 価格未確認
実行: 実行要件は未確認です。Agent・API・サービス料金を提供元で確認してください。
ライセンス: MPL-2.0
価格未確認: 価格は未確認です。既存のソースとインストールリンクは利用できます。

無料で入手できても実行が無料とは限りません。価格は安全評価ではありません。価格情報を送る →

スキルのソースを記録済み

手順のパスを記録しています。実行テスト、安全保証、互換性認証ではありません。

インストール前にレビュー: 自動インストールを避ける

ライセンス: 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

完全な監査を開く

ツール一覧はメタデータであり、互換性のテスト結果ではありません。プロンプトは提案です。

小さなタスクから始める

1ソースを読み、入力、出力、依存関係、権限を確認します。
2Agent に計画を求め、設定と費用を承認してから隔離環境でテストします。
3出力と変更ファイルを確認し、実行した結果だけを報告します。再現用にソースの版を保存します。

依存関係、API キー、外部サービスの料金をソースで確認してください。公開リポジトリでも全サービスが無料とは限りません。

出典と利用上の注意

登録済み

メタデータと審査情報は参考です。人気、ソースの発見、実行成功は別の事実です。

ソースリポジトリ: dstackai/dstack
ライセンス: MPL-2.0
バージョン: 1.0.0
最終 GitHub プッシュ: 2026年8月21日
登録情報の更新日: 2026年9月1日
手順のパス: skills/dstack-prototyping/SKILL.md

登録されたバージョンです。ソースのリリース情報を確認してください。

品質

77/100

強い

信頼

63/100

サンドボックス限定

監査

77/100

要レビュー

完全な監査を開く評価レポートを見る

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

Verified installs: —
成果: —

コピーはインストールではありません。件数は成功報告に基づき、品質全体を保証しません。

Agent 接続

Registry API 経由で判断、信頼、監査、ユースケース、インストールのシグナルを提供し、UI をスクレイピングせずに Agent が順位付けできます。

Install API を開く LLM テキスト形式 Manifest を開く自動解決プラン

詳細情報

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    "High-risk permission hints: Shell or command execution, Secrets or environment access",
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      "Audit: 77/100 Needs review",
      "Safety: 37/100 Avoid automatic install",
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    "api": "https://www.openagentskill.com/api/agent/skills/dstackai-dstack-prototyping",
    "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"
  }
}

クリエイター向け

掲載元

Registry により登録

申請可能

この掲載は公開ソースから登録されており、メンテナー申請が承認されるまで公式として表示されません。

作成者: dstackai
ソース: dstackai/dstack
インデックス作成者: OpenAgentSkill コミュニティインデックス

帰属は公開リポジトリまたは作成者プロフィールにリンクされています。作成者は掲載を申請して所有権シグナルを更新できます。

このスキルを申請

所有者の申請

このスキル掲載を申請

この Registry により登録掲載は dstackai に帰属していますが、まだ公式として表示されていません。申請すると、確認済み所有者シグナルが追加され、今後の公開、インストール、監査更新の信頼性が高まります。

共有キット

クリエイター被リンクキット

README にエビデンスバッジを追加

開発者がリポジトリを評価する場所で、正規掲載、現在の信頼・監査シグナル、実際の Agent-Proven エビデンスを表示します。

[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/dstackai-dstack-prototyping?metric=listed&label=Listed)](https://www.openagentskill.com/skills/dstackai-dstack-prototyping?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/dstackai-dstack-prototyping?metric=trust&label=Trust)](https://www.openagentskill.com/skills/dstackai-dstack-prototyping?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/dstackai-dstack-prototyping?metric=audit&label=Audit)](https://www.openagentskill.com/skills/dstackai-dstack-prototyping/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/dstackai-dstack-prototyping?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/dstackai-dstack-prototyping?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)

バッジをプレビュー監査を開くクリエイターキット

コミュニティシグナル

このスキルが Agent ワークフローに役立つかを共有してください。集約されたフィードバックがランキングを改善します。

概要