mcpbeat Sign in

Dstack Prototyping Agent Skill

| 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.

1k tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
2203
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/dstackai/dstack --skill dstack-prototyping

The instruction itself

6 sections, as written by the author

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.

Other skills for the same job

different authors, same section of the catalogue
Libtv Skill
by ComeOnOliver
×1

agent-im 会话技能 - 通过 liblib.tv 的 AI 能力生成和编辑图片/视频。覆盖场景包括:生成(文生图、文生视频、图生视频、做动画、画一个xxx、来段xxx)、编辑修改(把xxx换成yyy、去掉xxx、加上xxx、改成xxx、调整xxx、局部修改、改镜头)、风格转换(风格迁移、转绘、换风格)、视频续写延长、复刻视频/TVC/宣传片、短剧/短漫剧生成、音乐MV生成、产品广告/展示片制作、分镜/故事板设计、教育视频/短视频制作。当用户提到 liblib、libtv、上传参考图/视频、查看生成进度时也应触发。关键判断:只要用户的请求涉及 AI 图片或视频的创作、生成、编辑、修改,无论措辞如何(如"画只猫"、"做个海报"、"把纸船换成爱心"、"这个视频帮我改一下"、"帮我复刻这段视频"、"用这首歌做个MV"、"一句话生成短剧"),都必须触发此技能。

10k tokens scripts zh
Seedance
by ComeOnOliver
×1

This skill should be used when the user asks to "generate video prompts", "create Seedance prompts", "write video descriptions", mentions "Seedance", "seedance", "即梦", "即梦平台", "视频提示词", "视频生成", "AI视频", "短剧", "广告视频", "视频延长", or discusses video prompt engineering, AI video generation, or Seedance 2.0 workflows.

8k tokens zh
Video Prompting Guide
by ComeOnOliver
×1

Best practices and techniques for writing effective AI video generation prompts. Covers: Veo, Seedance, Wan, Grok, Kling, Runway, Pika, Sora prompting strategies. Learn: shot types, camera movements, lighting, pacing, style keywords, negative prompts. Use for: improving video quality, getting consistent results, professional video prompts. Triggers: video prompt, how to prompt video, veo prompts, video generation tips, better ai video, video prompt engineering, video prompt guide, video prompt template, ai video tips, video prompt best practices, video prompt examples, cinematography prompts

5k tokens
Voice Agent Expert
by ComeOnOliver
×1

This skill is a practical, 'use-it-while-debugging' reference for getting a LiveKit + Letta voice agent working reliably.

8k tokens scripts
Update Screenshots
by microsoft
vendor

Download screenshot baselines from the latest CI run and commit them. Use when asked to update, accept, or refresh component screenshot baselines from CI, or after the screenshot-test GitHub Action reports differences. This skill should be run as a subagent.

362 tokens
Reference Design Contract
by nexu-io

| Turn vague taste, screenshots, URLs, product notes, or "make it feel like this" references into a grounded DESIGN.md plus an implementation handoff. Use it before prototypes, decks, redesigns, or image remix work when the user needs a reusable visual direction rather than a one-off prompt.

3k tokens
Stitch::upload To Stitch
by google-labs-code
vendor

>- Upload local assets (images, mockups, extracted HTML, design markdown) to a Stitch project. ALWAYS use this skill when you need to upload visual assets, HTML pages, or design docs to Stitch, particularly when direct MCP tool calls fail or truncate due to base64 token limits.

3k tokens scripts
Youtube Video API Skill
by browser-act

This skill helps users automatically extract channel-level and video detail data from a specific YouTube channel via BrowserAct API. Agent should proactively apply this skill when users express needs like extracting channel video data, getting latest or popular videos from a YouTube channel, tracking competitor channel content, extracting video metrics such as views likes comments, retrieving subscriber count and channel info, monitoring posting cadence of a YouTube channel, gathering video data for content strategy analysis, getting earliest videos of a YouTube creator, analyzing engagement signals across a full channel, and downloading structured YouTube video details without manual scraping.

3k tokens scripts

How to use it

Copy the folder

Take dstackai/dstack-prototyping from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.