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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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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.

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File metadata

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.

View original text

---
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.

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Review before install: Avoid automatic install

License: 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

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Source & usage notes

Indexed

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: dstackai/dstack
License: MPL-2.0
Version: 1.0.0
Last GitHub push: Aug 21, 2026
Registry updated: Sep 1, 2026
Instruction path: skills/dstack-prototyping/SKILL.md

Version reported in registry metadata; check source releases before relying on it.

Quality

77/100

Strong

Trust

63/100

Sandbox only

Audit

77/100

Needs review

Open full audit View eval report

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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dstack-prototyping

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Price unconfirmed★ 2,216 GitHub starsRegistry updated · Sep 1, 2026agent-skill

Overview

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dstack Prototyping

Goal

Find a working dstack service configuration for the requested model.

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:

commands:
  - sleep infinity

or an equivalent idle 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.

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.

View source ↗

File metadata

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.

View original text

# dstack Prototyping

## 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:

## 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.

```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.

## 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.

Review the source

Price & running costs

Get the skill: Price unconfirmed
Run it: Requirements have not been confirmed. Check the source for agent, API and service charges.
License: MPL-2.0
Price unconfirmed: We have not confirmed a price for this skill. Existing source and install links remain available.

Free to get does not mean free to run. Price labels are not safety ratings. Submit pricing information →

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: 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

Open full audit

Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.

Start with one small task

1Read the source. Confirm the input, expected output, dependencies and permissions.
2Ask your agent for a plan. Approve setup and any costs before running a small isolated test.
3Check the output and changed files. Report only what actually ran; keep the source revision for reproduction.

Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.

Source & usage notes

Indexed

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: dstackai/dstack
License: MPL-2.0
Version: 1.0.0
Last GitHub push: Aug 21, 2026
Registry updated: Sep 1, 2026
Instruction path: skills/dstack-prototyping/SKILL.md

Version reported in registry metadata; check source releases before relying on it.

Quality

77/100

Strong

Trust

63/100

Sandbox only

Audit

77/100

Needs review

Open full audit View eval report

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: —
Outcomes: —

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Overview

dstack Prototyping

Goal

Choose Where To Run

Check Serving Sources

Use A Task Before Service

Verify As A Service

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Overview

dstack Prototyping

Goal

Choose Where To Run

Check Serving Sources

Use A Task Before Service

Verify As A Service

Review the source

Price & running costs

Start with one small task

Source & usage notes

Agent access

Related skills

For the creator

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