Dstack

审查 · 75
社区收录

Vendor-agnostic orchestration for training, inference and agentic workloads across NVIDIA, AMD, TPU, and Tenstorrent on clouds, Kubernetes, and bare metal.

Verified installs0
Stars2.2K
版本1.0.0
质量100/100 · 优秀
信任75/100 · 仅限沙盒
审计90/100 · 需审查

供给资产档案

编程与开发 Agent

代码审查、仓库分析、测试、CI、GitHub、DevOps 与开发工作流 Skill。

浏览赛道

场景

GitHub automation

I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.

适配 Agent

Claude Code + Cursor + CLI

适用于 Codex、Claude Code、Cursor、CLI 或自定义 Agent。

安装

就绪

npx skills add dstackai/dstack

维护状态

新鲜

距上次推送 1 天

风险

需审查

Dependency or permission surface needs review

GitHub 质量

2.2K

100/100 质量 · 83/100 信任

覆盖标签

编程GitHub automationagent-skillsskillsagentic-orchestration

审查说明

Dependency or permission surface needs review · Permission surface may require sandboxing

Agent 采用评分卡

一眼查看信任、审计与安装准备度

这些分数综合公开仓库元数据、OpenAgentSkill 审查信号、维护新鲜度与安装准备度。它用于候选筛选,不替代人工审查。

质量

优秀
100

高置信候选,具有较强的采用度与健康维护信号。

信任

仅限沙盒
75

有用但信任信号不足或混杂的候选项。在结果闭环证明任务匹配前,请保持在隔离工作区内使用。

审计

需审查
90

对安装准备度、安全元数据、维护情况与采用风险的机器可读审查。

OpenAgentSkill 信任评分 v5

安装前需人工审查

仅在沙盒中运行,并在用于真实工作前比较接近的替代方案。

PythonAI AgentsCodexClaude CodeCursor

Stars

2.2K 个 GitHub Stars

仓库活跃度

2.2K 个 Star,250 个 Fork

维护状态

距上次推送 1 天

许可证

MPL-2.0

安装

npx skills add dstackai/dstack

安装安全性

标准软件包或运行时安装路径

权限范围

secrets or environment access, shell or command execution

Agent 结果

暂未有 Agent 结果数据

文档

README/SKILL.md 上下文充分

风险摘要

生产前审查

  • 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

安装准备度

安装路径可用

  • 安装路径可用
  • 仓库证据可用
  • 已声明许可证
  • 暂无 Agent 验证结果证据

Agent 可读元数据

这个 Skill 的机器可读决策数据。

使用此区块或内嵌 JSON 判断 Agent 是否应安装该 Skill、选择替代方案,或先请求人工审查。

打开 JSON

适用任务

  • Local desktop 工作流
  • Claude Code 团队
  • 重视 GitHub 采用信号的团队
  • Navigate local resources

适用 Agent

PythonAI AgentsCodexClaude CodeCursorOpenAgentSkill CLICLI

安装决策

命令
npx skills add dstackai/dstack
策略
审查
人工审查

信任与风险

信任
75/100
审计
90/100
风险级别
需审查

结果闭环

端点
/api/agent/outcome
事件 ID
resolve
结果
5

安装命令

npx skills add dstackai/dstack

不适用场景

  • 需要厂商支持 SLA 的团队
  • 没有内部安全审查的高合规环境
  • 当前元数据中未发现重大风险信号
  • 高风险权限提示:Shell or command execution, Secrets or environment access
  • Dependency or permission surface needs review

Agent 安全 v2

50/100 · 避免自动安装

实验性审查

Sparse or mixed signals. Useful for discovery, but not for autonomous installation.

Test manually in an isolated workspace and compare against safer alternatives.

通过 API 解析

Shell 或命令执行

Skill 元数据引用了终端、CLI、Shell、子进程或命令执行工作流。

网络访问

Skill 可能访问远程页面、API、仓库或外部服务。

文件系统访问

Skill 可能读取或写入项目文件、文档、生成产物或本地工作区状态。

Secrets or environment access

Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.

  • 高风险权限提示:Shell or command execution, Secrets or environment access
  • Dependency or permission surface needs review

安装目标

在你的 Agent 工作流中安装此 Skill

通过公开安装端点获取命令、安全清单、目标提示词和该 Skill 的规范链接。

skill install

OpenAgentSkill CLI

Resolve policy, run the source installer safely, and report a verified install receipt.

$ npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.2.1/openagentskill-0.2.1.tgz install dstackai-dstack

Agent 解析计划

让 Agent 在安装前验证匹配度。

Resolve API 返回首选 Skill、替代方案、安全策略、审计说明、安装目标和可直接执行的提示词,无需抓取此页面。

打开文本计划

Agent 应检查

  • 从 Resolve API 检查任务匹配与替代方案。
  • 检查审计评分、信任评分和安全策略警告。
  • 检查 Codex、Claude Code、Cursor 或 CLI 的安装目标兼容性。

复制提示词

Task: Use Dstack in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20Dstack%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/dstackai-dstack/install
Install command: npx skills add dstackai/dstack
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.

Agent 交接

把安装路径交给 Agent,而不是再给一个目录页。

通过公开安装端点获取命令、安全清单、目标提示词和该 Skill 的规范链接。

打开安装 API

Agent 提示词

Use Dstack for this task. Review https://www.openagentskill.com/api/skills/dstackai-dstack/install, then install with: npx skills add dstackai/dstack

Registry 元数据

用于自动选择 Skill 的 Agent 可读档案。

本页通过 Registry API 提供相同的决策、信任、审计、场景和安装信号,让 Agent 无需抓取界面即可排序。

打开 Manifest

适配 Agent

100/100

Local desktop

平台

Python, AI Agents, Claude Code, Cursor

审计报告

需审查 · 90/100

对安装准备度、安全元数据、维护情况与采用风险的机器可读审查。

查看审计报告查看评估报告

Agent 决策面板

适合 Local desktop 的首选

将其作为优先候选,再在你的 Agent 环境中验证 README 与安装路径。

100
就绪度
采用
阶段

栈中角色

首选

主要匹配

Local desktop

信任标签

可用于生产

安装路径

命令已就绪

适用场景

  • Local desktop 工作流
  • Claude Code 团队
  • 重视 GitHub 采用信号的团队

证据

  • 2,216 个 GitHub Stars
  • 仓库近期活跃
  • 已提供安装命令或 GitHub 仓库
  • 100/100 质量档案
  • 19 个 OpenAgentSkill 交互事件

先审查

  • 当前元数据中未发现重大风险信号

实施路径

  1. 1在沙盒 Agent 中安装它,并端到端完成一次Local desktop任务。
  2. 2Compare output quality, latency, and failure behavior against at least one alternative.
  3. 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.

信任档案

仅限沙盒

有用但信任信号不足或混杂的候选项。在结果闭环证明任务匹配前,请保持在隔离工作区内使用。

75
OpenAgentSkill 信任评分

GitHub 采用度

通过

2.2K 个 GitHub Stars

Star/Fork 活跃度

通过

2.2K 个 Star,250 个 Fork; 当前元数据中没有议题活跃度信息

近期维护

通过

距上次推送 1 天

许可证清晰度

通过

MPL-2.0

积极信号

  • 人工验证的收录
  • AI 审查已通过
  • 安装路径可用
  • 仓库证据可用
  • 近期维护的仓库
  • 有意义的 GitHub 采用信号
  • 安装命令未发现明显高风险模式
  • 结果闭环已就绪,但需要首次真实 Agent 运行

安装前审查

  • 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
  • 暂未有真实 Agent 结果报告
  • 无人值守安装前需要人工审查

建议操作

仅在沙盒中运行,并在用于真实工作前比较接近的替代方案。

质量档案

优秀 适用于 Agent 工作流的候选

高置信候选,具有较强的采用度与健康维护信号。

100
GitHub Stars
2.2K
新鲜度
1 天前
安装就绪
许可证
MPL-2.0

工作流匹配

在这些场景使用此 Skill

工作流匹配

加入完整工作流

替代方案短名单

安装前对比

可能适合该任务的相近 Skill。

对比全部

概览

--- name: dstack description: | dstack is an open-source control plane for GPU provisioning and orchestration across GPU clouds, Kubernetes, and on-prem clusters. ---

# dstack

## Overview

`dstack` provisions and orchestrates workloads across GPU clouds, Kubernetes, and on-prem via fleets.

**When to use this skill:** - Running or managing dev environments, tasks, or services on dstack - Creating, editing, or applying `*.dstack.yml` configurations - Managing fleets, volumes, gateways, and checking available offers

## How it works

`dstack` operates through three core components:

1. `dstack` server - Can run locally, remotely, or via dstack Sky (managed) 2. `dstack` CLI - Applies configurations and manages or inspects fleets, runs, logs, events, volumes, gateways, and offers; it uses project configurations stored in `~/.dstack/config.yml`, which can be managed with `dstack project` 3. `dstack` configuration files - YAML files ending with `.dstack.yml`

`dstack apply` shows a plan and submits configuration changes. For run configurations, it attaches when the run reaches `running` by default: it configures SSH access, forwards declared ports, and streams logs. With `-d`, it submits and exits.

## Quick agent flow (detached runs)

1) Show plan: `echo "n" | dstack apply -f <config>` 2) If plan is OK and user confirms, apply detached: `dstack apply -f <config> -y -d` 3) Check the run: `dstack run get <run-name> --json` 4) If dev-environment or task with ports and running: attach to surface IDE link/ports/SSH alias (agent runs attach in background); ask to open link 5) If attach fails in sandbox: request escalation; if not approved, ask the user to run `dstack attach` locally and share the output

**CRITICAL: Never propose `dstack` CLI commands or YAML syntaxes that don't exist.** - Only use CLI commands and YAML syntax documented here or verified via `--help` - If uncertain about a command or its syntax, check the links or use `--help`

**NEVER do the following:** - Invent CLI flags not documented here or shown in `--help` - Guess YAML property names - verify in configuration reference links - Run `dstack apply` for runs without `-d` in automated contexts (blocks indefinitely) - Retry failed commands without addressing the underlying error - Summarize or reformat tabular CLI output - show it as-is - Use `echo "y" |` when `-y` flag is available - Assume a command succeeded without checking output for errors

## Agent execution guidelines

### Output accuracy - **NEVER reformat, summarize, or paraphrase CLI output.** Display tables, status output, and error messages exactly as returned. - When showing command results, use code blocks to preserve formatting. - If output is truncated due to length, indicate this clearly (e.g., "Output truncated. Full output shows X entries.").

### Verification before execution - **When uncertain about any CLI flag or YAML property, run `dstack <command> --help` first.** - Never guess or invent flags. Example verification commands: ```bash dstack --help # List all commands dstack apply -h <configuration type> # Flags for apply per configuration type (dev-environment, task, service, fleet, etc) dstack fleet --help # Fleet subcommands dstack ps --help # Flags for ps ``` - If a command or flag isn't documented, it doesn't exist.

### Command timing and confirmation handling

**Commands that stream indefinitely in the foreground:** - `dstack attach` - `dstack apply` without `-d` for runs - `dstack ps -w`

Agents should avoid blocking: use `-d`, timeouts, or background attach. When attach is needed, run it in the background by default (`nohup ...`), but describe it to the user simply as "attach" unless they ask for a live foreground session.

When waiting programmatically for a specific run, use `dstack run get <run-name> --json` and read its top-level `status`. Run statuses are `pending`, `submitted`, `provisioning`, `running`, `terminating`, `terminated`, `failed`, and `done`; the last three are terminal. Stop waiting when the run reaches the state needed for the next action or a terminal status. Never parse or grep human-readable `dstack ps` output; its status column may display a job message such as `no offers`.

**All other commands:** Use 10-60s timeout. Most complete within this range. **While waiting, monitor the output** - it may contain errors, warnings, or prompts requiring attention.

**Confirmation handling:** - `dstack apply`, `dstack stop`, `dstack fleet delete` require confirmation - Use `-y` flag to auto-confirm when user has already approved - For `dstack stop`, always use `-y` after the user confirms to avoid interactive prompts - Use `echo "n" |` to preview `dstack apply` plan without executing (avoid `echo "y" |`, prefer `-y`)

**Best practices:** - Prefer modifying configuration files over passing parameters to `dstack apply` (unless it's an exception) - When user confirms deletion/stop operations, use `-y` flag to skip confirmation prompts

### Detached run follow-up (after `-d`)

After submitting a run with `-d` (dev-environment, task, service), first determine whether submission failed. If the apply output shows errors (validation, no offers, etc.), stop and surface the error.

If the run was submitted, check it with `dstack run get <run-name> --json`, then guide the user through relevant next steps: If you need to prompt for next actions, be explicit about the dstack step and command (avoid vague questions). When speaking to the user, refer to the action as "attach" (not "background attach"). - **Monitor status:** Report the current status and offer to keep watching. If watching, poll `dstack run get <run-name> --json` every 10-20 seconds until it reaches the state needed for the next action or a terminal status. - **Attach when running:** For agents, run attach in the background by default so the session does not block. Use it to capture IDE links/SSH alias or enable port forwarding; when describing the action to the user, just say "attach". - **Dev environments or tasks with ports:** Once `running`, attach to surface the IDE link/port forwarding/SSH alias, then ask whether to open the IDE link. Never open links without explicit approval. - **Services:** Prefer using service endpoints. Attach only if the user explicitly needs port forwarding or full log replay. - **Tasks without ports:** Default to `dstack logs` for progress; attach only if full log replay is required.

### Attaching behavior (blocking vs non-blocking)

`dstack attach` runs until interrupted and blocks the terminal. **Agents must avoid indefinite blocking.** If a brief attach is needed, use a timeout to capture initial output (IDE link, SSH alias) and then detach.

Note: `dstack attach` writes SSH alias info under `~/.dstack/ssh/config` (and may update `~/.ssh/config`) to enable `ssh <run name>`, IDE connections, port forwarding, and real-time logs (`dstack attach --logs`). If the sandbox cannot write there, the alias will not be created.

**Permissions guardrail:** If `dstack attach` fails due to sandbox permissions, request permission escalation to run it outside the sandbox. If escalation isn’t approved or attach still fails, ask the user to run `dstack attach` locally and share the IDE link/SSH alias output.

**Background attach (non-blocking default for agents):** ```bash nohup dstack attach <run name> --logs > /tmp/<run name>.attach.log 2>&1 & echo $! > /tmp/<run name>.attach.pid ``` Then read the output: ```bash tail -n 50 /tmp/<run name>.attach.log ``` Offer live follow only if asked: ```bash tail -f /tmp/<run name>.attach.log ``` Stop the background attach (preferred): ```bash kill "$(cat /tmp/<run name>.attach.pid)" ``` If the PID file is missing, fall back to a specific match (avoid killing all attaches): ```bash pkill -f "dstack attach <run name>" ``` **Why this helps:** it keeps the attach session alive (including port forwarding) while the agent remains usable. IDE links and SSH instructions appear in the log file -- surface them and ask whether to open the link (`open "<link>"` on macOS, `xdg-open "<link>"` on Linux) only after explicit approval.

If background attach fails in the sandbox (permissions writing `~/.dstack` or `~/.ssh`, timeouts), request escalation to run attach outside the sandbox. If not approved, ask the user to run attach locally and share the IDE link/SSH alias.

### Interpreting user requests

**"Run something":** When the user asks to run a workload (dev environment, task, service), use `dstack apply` with the appropriate configuration. Note: `dstack run` only supports `dstack run get --json` for retrieving run details -- it cannot start workloads.

**"Connect to" or "open" a dev environment:** If a dev environment is already running, use `dstack attach <run name> --logs` (agent runs it in the background by default) to surface the IDE URL (`cursor://`, `vscode://`, etc.) and SSH alias. If sandboxed attach fails, request escalation or ask the user to run attach locally and share the link.

## Configuration types

`dstack` supports run configurations (dev environments, tasks, and services) and infrastructure configurations (fleets, volumes, and gateways). Configuration files can be named `<name>.dstack.yml` or simply `.dstack.yml`.

**Common parameters:** All run configurations (dev environments, tasks, services) support many parameters including: - **Git integration:** Clone repos automatically (`repo`) or mount existing repos (`repos`) - **File upload:** Upload local files (`files`; see concept docs for examples) - **Docker support:** Use custom Docker images (`image`); use `docker: true` if you want to use Docker from inside the container (VM-based backends only) - **Environment:** Set environment variables (`env`), often via `.envrc`. Secrets are supported but less common. - **Storage:** Persistent network volumes (`volumes`), specify disk size - **Resources:** Define GPU, CPU, memory, and disk requirements

**Best practices:** - Prefer giving configurations a `name` property for easier management - When configurations need credentials (API keys, tokens), list only env var names in the `env` section (e.g., `- HF_TOKEN`), not values. Recommend storing actual values in a `.envrc` file alongside the configuration, applied via `source .envrc && dstack apply`. - `python` and `image` are mutually exclusive in run configurations. If `image` is set, do not set `python`.

### `files` and `repos` intent policy

Use `files` and `repos` only when the user intends to use local/repo files inside the run.

- If user asks to use project code/data/config in the run, then add `files` or `repos` as appropriate. - If it is totally unclear whether files or repos must be mounted, ask one explicit clarification question or default to not mounting.

`files` guidance: - Relative paths are valid and preferred for local project files. - A relative `files` path is placed under the run's `working_dir` (default or set by user).

`repos` + image/working directory guidance: - With non-default Docker images, prefer explicit absolute mount targets for `repos` (e.g., `.:/dstack/run`). - When setting an explicit repo mount path, also set `working_dir` to the same path. - Reason: custom images may have a different/non-empty default working directory, and mounting a repo into a non-empty path can fail. - With `dstack` default images, the default `working_dir` is already `/dstack/run`.

### 1. Dev environments **Use for:** Interactive development with IDE integration (VS Code, Cursor, etc.).

```yaml type: dev-environment name: cursor

python: "3.12" ide: vscode

resources: gpu: 80GB ```

[Concept documentation](https://dstack.ai/docs/concepts/dev-environments.md) | [Configuration reference](https://dstack.ai/docs/reference/dstack.yml/dev-environment.md)

### 2. Tasks **Use for:** Batch jobs, training runs, fine-tuning, web applications, any exe

平台兼容性

pythonFULL
ai-agentsFULL

技术详情

版本
1.0.0
许可证
MPL-2.0
最近更新
2026年8月21日
发布时间
2026年6月6日

框架与工具

PythonAI Agents

决策摘要

首选

100
就绪
采用
阶段

2,216 个 GitHub Stars

审计

安装审查

安装与采用审查

90
需审查
安全性
77/100
维护状态
100/100
安装
92/100
打开完整审计查看评估报告

Agent 验证证据

Agent 验证证据

来自解析、审查、安装和一次小范围运行后的结果报告。

0
已验证
Needs first agent run自动安装: 先审查最近: 未知
成功率
近期失败
结果
0
输出质量
失败
0
不相关
0
安装次数
0
风险拦截
0
需要配置
0
生产环境
0

暂时没有 Agent 结果数据。首次 Agent 执行可以通过 /api/agent/outcome 报告成功、需要设置、风险拦截、失败或不相关。

安装

加入 Agent 工作流

免费且开源. 在生产 Agent 中安装前请先审查报告。

增长闭环

分享工具包

X

为 Dstack 准备的场景化草稿,可手动发布到 X。

策展说明
Dstack: Vendor-agnostic orchestration for training, inference and agentic workloads across NVIDIA, AM...

2.2K stars

https://www.openagentskill.com/skills/dstackai-dstack?ref=x
打开 X 草稿
可选:带安装命令的回复
Listing + install path for Dstack:
https://www.openagentskill.com/skills/dstackai-dstack?ref=x

Install: npx skills add dstackai/dstack
打开回复草稿

收录来源

社区收录

可认领

此列表来自公开来源,维护者认领获批前不会标记为官方。

创作者
dstackai
收录方
OpenAgentSkill 社区索引

归属链接指向公开仓库或创作者主页。创作者可认领列表以更新所有权信号。

认领此 Skill

所有者认领

认领此 Skill 页面

这条 社区收录 列表归属于 dstackai,但尚未标记为官方。认领后可增加已验证所有者信号,使后续发布、安装和审计更新更值得信赖。

创作者外链工具包

将证据徽章加入你的 README

在开发者评估仓库的位置展示规范页面、当前信任与审计信号,以及真实的 Agent 验证证据。

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

作者

D

dstackai

@dstackai

平台适配

健康信号

GitHub Stars
2.2K
质量评分
65/100
最近 GitHub 推送
2026年8月21日
框架提示
2
OpenAgentSkill 浏览量
14
复制安装命令
0
跳转点击
0

社区信号

告诉我们这个 Skill 是否对你的 Agent 工作流有帮助。汇总反馈会持续改善排序。

信任与安全

仅限沙盒

75
  • GitHub 采用度2.2K 个 GitHub Stars通过
  • Star/Fork 活跃度2.2K 个 Star,250 个 Fork; 当前元数据中没有议题活跃度信息通过
  • 近期维护距上次推送 1 天通过
  • 许可证清晰度MPL-2.0通过
  • README/SKILL.md 完整度元数据包含足够的用法与工作流上下文通过
  • 依赖与运行时风险command execution surface, credential or environment access修复