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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
Übersicht
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/andhttps://recipes.vllm.ai/models.json - SGLang docs:
https://docs.sglang.io/(fetch/llms.txtfor the page index) - SGLang model recipes:
https://docs.sglang.io/cookbook/autoregressive/intro - Release notes:
https://github.com/vllm-project/vllm/releasesandhttps://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.
Dateimetadaten
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.
Originaltext anzeigen
--- 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.
Quelle prüfen
Preis und Betriebskosten
- Skill beziehen
- Preis unbestätigt
- Ausführen
- Anforderungen unbestätigt. Agenten-, API- und Dienstkosten an der Quelle prüfen.
- Lizenz
- MPL-2.0
- Preis unbestätigt
- Der Preis ist noch nicht bestätigt. Vorhandene Quell- und Installationslinks bleiben verfügbar.
Kostenloser Bezug bedeutet nicht kostenlosen Betrieb. Preise sind keine Sicherheitsbewertung. Preisinformation einreichen →
Skill-Quelle erfasst
Ein Anleitungspfad ist erfasst. Das ist kein Ausführungstest und keine Sicherheits- oder Kompatibilitätsgarantie.
Vor Installation prüfen: Automatische Installation vermeiden
Lizenz: 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
Tools sind Metadatenhinweise, keine getestete Kompatibilität. Prompts sind Vorschläge.
Mit einer kleinen Aufgabe beginnen
- 1Quelle lesen und Eingaben, Ergebnisse, Abhängigkeiten sowie Berechtigungen prüfen.
- 2Agent um einen Plan bitten. Einrichtung und Kosten vor einem isolierten Test genehmigen.
- 3Ergebnisse und geänderte Dateien prüfen. Nur tatsächliche Ausführungen melden und die Quellrevision aufbewahren.
Prüfe Abhängigkeiten, API-Schlüssel und externe Kosten in der Quelle. Öffentliche Repositories bedeuten nicht, dass alle Dienste kostenlos sind.
Quelle und Nutzungshinweise
Metadaten und Prüfungen dienen der Orientierung. Beliebtheit, Quellenerfassung und erfolgreiche Ausführung sind verschiedene Fakten.
- Quell-Repository
- dstackai/dstack
- Lizenz
- MPL-2.0
- Version
- 1.0.0
- Letzter GitHub-Push
- 21. Aug. 2026
- Verzeichnis aktualisiert
- 1. Sept. 2026
- Anleitungspfad
- skills/dstack-prototyping/SKILL.md
Version aus den Verzeichnismetadaten; Releases der Quelle prüfen.
Qualität
77/100
Stark
Vertrauen
63/100
Nur Sandbox
Audit
77/100
Prüfung nötig
- 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
- —
- Ergebnisse
- —
Kopieren ist keine Installation. Zahlen benötigen eine Erfolgsmeldung und garantieren keine allgemeine Qualität.
Agent-Zugang
Die Registry API stellt Entscheidungs-, Vertrauens-, Audit-, Use-Case- und Installationssignale ohne UI-Scraping bereit.
Weitere Details
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}Für Ersteller
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Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.
- Ersteller
- dstackai
- Quelle
- dstackai/dstack
- Indexiert von
- OpenAgentSkill Community-Index
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Share-Kit
Creator-Backlink-Kit
Evidenz-Badges in deine README einfügen
Zeige den kanonischen Eintrag, aktuelle Vertrauens- und Audit-Signale sowie echte Agent-Proven-Evidenz dort, wo Entwickler das Repository bewerten.
[](https://www.openagentskill.com/skills/dstackai-dstack-prototyping?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/dstackai-dstack-prototyping?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/dstackai-dstack-prototyping/audit)
[](https://www.openagentskill.com/skills/dstackai-dstack-prototyping?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)Community-Signal
Teile mit, ob dieser Skill für deinen Agent-Workflow nützlich ist. Zusammengefasstes Feedback verbessert das Ranking im Laufe der Zeit.