Paidf Orchestration Setup
NVIDIA/skills
Audit, prepare, and deploy PAIDF Orchestration on a Kubernetes GPU cluster - single-GPU H100/L40S hosts, managed Kubernetes, kubeadm, and similar.
dstack is an open-source control plane for GPU provisioning and orchestration across GPU clouds, Kubernetes, and on-prem clusters.
The automated check flagged lines worth reading first. See the safety section below.
$ npx skills add dstackai/dstack --skill dstack -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install dstackai/dstack dstack --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/dstackai/dstack.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/dstack .claude/skills/dstack && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "dstack" agent skill from https://github.com/dstackai/dstack/tree/master/skills/dstack into .claude/skills/dstack/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dstack", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/dstackai/dstack/tree/master/skills/dstackType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add dstackai/dstack --skill dstack -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install dstackai/dstack dstack --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/dstackai/dstack.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/dstack .agents/skills/dstack && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "dstack" agent skill from https://github.com/dstackai/dstack/tree/master/skills/dstack into .agents/skills/dstack/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dstack", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add dstackai/dstack --skill dstack -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install dstackai/dstack dstack --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/dstackai/dstack.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/dstack .cursor/skills/dstack && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "dstack" agent skill from https://github.com/dstackai/dstack/tree/master/skills/dstack into .cursor/skills/dstack/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dstack", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/dstackai/dstack.git --path skills/dstack--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add dstackai/dstack --skill dstack -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install dstackai/dstack dstack --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/dstackai/dstack.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/dstack .gemini/skills/dstack && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "dstack" agent skill from https://github.com/dstackai/dstack/tree/master/skills/dstack into .gemini/skills/dstack/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dstack", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install dstackai/dstack dstackInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add dstackai/dstack --skill dstack -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/dstackai/dstack.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/dstack .github/skills/dstack && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "dstack" agent skill from https://github.com/dstackai/dstack/tree/master/skills/dstack into .github/skills/dstack/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dstack", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add dstackai/dstack --skill dstack -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install dstackai/dstack dstack --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/dstackai/dstack.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/dstack .opencode/skills/dstack && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "dstack" agent skill from https://github.com/dstackai/dstack/tree/master/skills/dstack into .opencode/skills/dstack/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dstack", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
dstackdstack is an open-source control plane for GPU provisioning and orchestration across GPU clouds, Kubernetes, and on-prem clusters.
Dstack is an agent skill from dstackai/dstack. dstack is an open-source control plane for GPU provisioning and orchestration across GPU clouds, Kubernetes, and on-prem clusters.
Its SKILL.md is about 6.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in DevOps & Cloud, covering Container orchestration and GPU and accelerator computing. It works with Kubernetes, NVIDIA AI Platform, Docker and Python. The repository describes itself as: A unified orchestration layer for heterogeneous AI compute. It standardizes how to manage compute and run training and inference on GPU clouds, Kubernetes, VMs, or bare-metal… The licence is MPL-2.0.
6 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 0d578c8. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
sshcurlFrom the folder's file list and the shell code blocks in SKILL.md.
Links to these hosts (documentation or services it may open):
dstack.aiFrom URLs in SKILL.md, links to its own repository left out.
Names these keys or tokens, usually read from environment variables:
HF_TOKENHUGGING_FACE_HUB_TOKENFrom names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Dstack loads about 6.2k tokens when it runs. Until then it costs about 34 tokens; SKILL.md has 2,597 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found patterns that need a careful read before installing.
`~/.dstack/ssh/config` (and may update `~/.ssh/config`) to enable `ssh <run name>`, IDE connections, port forwarding, anox (permissions writing `~/.dstack` or `~/.ssh`, timeouts), request escalation to run attach outside the sandbox. If notidentity_file: ~/.ssh/id_rsaAutomated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from dstackai/dstack at commit 0d578c8, republished under its MPL-2.0 licence (© dstackai). 2,597 words, ~6,164 tokens.
.claude/skills/dstack/SKILL.md (or your agent's skills folder).dstack provisions and orchestrates workloads across GPU clouds, Kubernetes, and on-prem via fleets.
When to use this skill:
*.dstack.yml configurationsdstack operates through three core components:
dstack server - Can run locally, remotely, or via dstack Sky (managed)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 projectdstack configuration files - YAML files ending with .dstack.ymldstack 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.
echo "n" | dstack apply -f <config>dstack apply -f <config> -y -ddstack run get <run-name> --jsondstack attach locally and share the outputCRITICAL: Never propose dstack CLI commands or YAML syntaxes that don't exist.
--help--helpNEVER do the following:
--helpdstack apply for runs without -d in automated contexts (blocks indefinitely)echo "y" | when -y flag is availabledstack <command> --help first.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 psCommands that stream indefinitely in the foreground:
dstack attachdstack apply without -d for runsdstack ps -wAgents 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-y flag to auto-confirm when user has already approveddstack stop, always use -y after the user confirms to avoid interactive promptsecho "n" | to preview dstack apply plan without executing (avoid echo "y" |, prefer -y)Best practices:
dstack apply (unless it's an exception)-y flag to skip confirmation prompts-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").
dstack run get <run-name> --json every 10-20 seconds until it reaches the state needed for the next action or a terminal status.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.dstack logs for progress; attach only if full log replay is required.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):
nohup dstack attach <run name> --logs > /tmp/<run name>.attach.log 2>&1 & echo $! > /tmp/<run name>.attach.pidThen read the output:
tail -n 50 /tmp/<run name>.attach.logOffer live follow only if asked:
tail -f /tmp/<run name>.attach.logStop the background attach (preferred):
kill "$(cat /tmp/<run name>.attach.pid)"If the PID file is missing, fall back to a specific match (avoid killing all attaches):
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.
"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.
Unless you use Multi-node tasks (see ### 2. Tasks) or Multi-replica services (see ### 3. Services), both tasks and services run on a single node. That's why dstack logs <run name>, dstack attach <run name>, and ssh <run name> default to the first replica/job.
dstack logs <run name> --job 1 or dstack attach <run name> --job 1.dstack logs <run name> --replica 1 or dstack attach <run name> --replica 1.--job or --replica creates the SSH alias ssh <run name>-<job num>-<replica num>.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:
repo) or mount existing repos (repos)files; see concept docs for examples)image); use docker: true if you want to use Docker from inside the container (VM-based backends only)env), often via .envrc. Secrets are supported but less common.volumes), specify disk sizeBest practices:
name property for easier managementenv 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 policyUse files and repos only when the user intends to use local/repo files inside the run.
files or repos as appropriate.files guidance:
files path is placed under the run's working_dir (default or set by user).repos + image/working directory guidance:
repos (e.g., .:/dstack/run).working_dir to the same path.dstack default images, the default working_dir is already /dstack/run.Use for: Interactive development with IDE integration (VS Code, Cursor, etc.).
type: dev-environment
name: cursor
python: "3.12"
ide: vscode
resources:
gpu: 80GBConcept documentation | Configuration reference
Use for: Batch jobs, training runs, fine-tuning, web applications, any executable workload.
Key features: Distributed training (multi-node) and port forwarding for web apps.
type: task
name: train
python: "3.12"
env:
- HUGGING_FACE_HUB_TOKEN
commands:
- uv pip install -r requirements.txt
- uv run python train.py
ports:
- 8501 # Optional: expose ports for web apps
resources:
gpu: A100:40GB:2Port forwarding: When you specify ports, dstack apply forwards them to localhost while attached. Use dstack attach <run name> to reconnect and restore port forwarding. The run name becomes an SSH alias (e.g., ssh <run name>) for direct access.
Multi-node tasks: Set nodes to run a task across multiple nodes, or use groups to define node groups, each with its own nodes count, resources, commands, and ports (groups and top-level nodes are mutually exclusive). Requires a fleet that supports inter-node communication (see placement: cluster in fleets).
Concept documentation | Configuration reference
Use for: Deploying models or web applications as production endpoints.
Key features: OpenAI-compatible model serving, auto-scaling (RPS/queue), custom gateways with HTTPS.
type: service
name: llama31
python: "3.12"
env:
- HF_TOKEN
commands:
- uv pip install vllm
- uv run vllm serve meta-llama/Meta-Llama-3.1-8B-Instruct
port: 8000
model: meta-llama/Meta-Llama-3.1-8B-Instruct
resources:
gpu: 80GB
disk: 200GBService endpoints:
<server URL>/proxy/services/<project name>/<run name>/https://<run name>.<gateway domain>/auth is false, include Authorization: Bearer <user token> on service requests.model is set, service.model.base_url from dstack run get <run name> --json provides the model endpoint. For OpenAI-compatible models (the default, unless format is set otherwise), this will be service.url + /v1.curl -sS -X POST "https://<run name>.<gateway domain>/v1/chat/completions" \
-H "Authorization: Bearer <user token>" \
-H "Content-Type: application/json" \
-d '{"model":"<model name>","messages":[{"role":"user","content":"Hello"}],"max_tokens":64}'Multi-replica services: Set replicas to run multiple replicas, or use groups to define replica groups, each with its own replicas count, resources, and commands (groups and top-level replicas are mutually exclusive). If replicas require an interconnect (e.g., PD disaggregation), the service must run on a fleet with placement: cluster.
Concept documentation | Configuration reference
Use for: Pre-provisioning infrastructure for workloads, managing on-prem GPU servers, creating auto-scaling instance pools.
type: fleet
name: my-fleet
nodes: 0..2
resources:
gpu: 24GB..
disk: 200GB
spot_policy: auto # other values: spot, on-demand
idle_duration: 5mOn-demand provisioning: When nodes is a range (e.g., 0..2), dstack creates a template and provisions instances on demand within the min/max. Use idle_duration to terminate idle instances.
Distributed workloads: Use placement: cluster for fleets intended for multi-node tasks that require inter-node networking.
SSH fleet (on-prem or pre-provisioned):
type: fleet
name: on-prem-fleet
ssh_config:
user: ubuntu
identity_file: ~/.ssh/id_rsa
hosts:
- 192.168.1.10
- 192.168.1.11Concept documentation | Configuration reference
Use for: Persistent storage for datasets, model checkpoints, training artifacts.
type: volume
name: my-volume
backend: aws
region: us-east-1
resources:
disk: 500GBInstance volumes (local, ephemeral, often optional):
type: dev-environment
# ... other config
volumes:
- instance_path: /dstack-cache/pip
path: /root/.cache/pip
optional: true
- instance_path: /dstack-cache/huggingface
path: /root/.cache/huggingface
optional: trueMounting volumes: Use volumes in dev environments, tasks, and services. Network volumes persist independently; instance volumes are tied to the instance lifecycle.
Concept documentation | Configuration reference
Use for: Gateways are optional for basic service endpoints. They are required when a service uses auto-scaling or rate limits, needs HTTPS on a custom domain, requires WebSockets, or cannot work with the server proxy path prefix.
type: gateway
name: my-gateway
backend: aws
region: us-east-1
domain: example.comConcept documentation | Configuration reference
Important behavior:
dstack apply shows a plan with estimated costs and may ask for confirmation-d), it submits and exits without attachingWorkflow for applying run configurations (dev-environment, task, service):
Show plan:
echo "n" | dstack apply -f config.dstack.ymlDisplay the FULL output including the offers table and cost estimate. Do NOT summarize or reformat.
Wait for user confirmation. Do NOT proceed if:
Execute (only after user confirms):
dstack apply -f config.dstack.yml -y -dVerify apply status:
dstack run get <run-name> --jsonWorkflow for infrastructure (fleet, volume, gateway):
Show plan:
echo "n" | dstack apply -f infra.dstack.ymlDisplay the FULL output. Do NOT summarize or reformat.
Wait for user confirmation.
Execute:
dstack apply -f infra.dstack.yml -yVerify: Use dstack fleet, dstack volume, or dstack gateway respectively.
# Create/update fleet
dstack apply -f fleet.dstack.yml
# List fleets
dstack fleet
# Get fleet details
dstack fleet get my-fleet
# Get fleet details as JSON (for troubleshooting)
dstack fleet get my-fleet --json
# Delete entire fleet (use -y when user already confirmed)
dstack fleet delete my-fleet -y
# Delete specific instance from fleet (use -y when user already confirmed)
dstack fleet delete my-fleet -i <instance num> -yHost GPU driver: for NVIDIA, AMD, and Tenstorrent, VM-based backends and SSH fleets report the host GPU driver version in instances[].gpu_driver.version, available via dstack fleet get my-fleet --json once the instance is idle or busy. Useful when you're uncertain whether a host's driver is compatible with your workload.
# List all runs
dstack ps
# Verbose output with full details
dstack ps -v
# JSON output (for troubleshooting/scripting)
dstack ps --json
# Get specific run details as JSON
dstack run get my-run-name --json# Attach and replay logs from start (preferred, unless asked otherwise)
dstack attach my-run-name --logs
# Attach without replaying logs (restores port forwarding + SSH only)
dstack attach my-run-name# Stream logs (tail mode)
dstack logs my-run-name
# Debug mode (includes additional runner logs)
dstack logs my-run-name -d
# Fetch logs from specific replica (multi-node runs)
dstack logs my-run-name --replica 1
# Fetch logs from specific job
dstack logs my-run-name --job 0# Stop specific run (use -y after user confirms)
dstack stop my-run-name -y
# Abort (force stop)
dstack stop my-run-name --abortOffers represent available instance configurations that match resource
requirements. If --fleet is omitted, dstack offer checks all configured
backends. Listing offers does not create capacity; submitting a run still
requires at least one fleet that can provision or reuse matching instances.
Use --fleet to inspect offers available through specific fleets.
# Filter by specific backend
dstack offer --backend aws
# Filter by GPU type
dstack offer --gpu A100
# Filter by GPU memory
dstack offer --gpu 24GB..80GB
# Combine filters
dstack offer --backend aws --gpu A100:80GB
# Limit to a specific fleet
dstack offer --fleet my-fleet
# Combine offers from multiple fleets
dstack offer --fleet my-fleet --fleet other-fleet
# JSON output (for troubleshooting/scripting)
dstack offer --jsonWith one --fleet, dstack offer shows offers available through that fleet. With multiple --fleet, it combines offers available through the selected fleets. Identical backend offers are shown once, while matching existing instances stay separate.
Max offers: By default, dstack offer returns first N offers (output also
includes the total number). Use --max-offers N to increase the limit.
Grouping: Prefer --group-by gpu for aggregated output across all offers,
not --max-offers. Other supported fields are backend, region, and
count; region requires backend.
When diagnosing issues with dstack workloads or infrastructure:
Use JSON output for detailed inspection:
dstack fleet get my-fleet --json
dstack run get my-run --json
dstack ps -n 10 --json
dstack offer --jsonCheck verbose run status:
dstack ps -vExamine logs with debug output:
dstack logs my-run -dAttach with log replay:
dstack attach my-run --logsCommon issues:
dstack offer; if submitting a run, ensure at least one fleet can provision or reuse matching instancesdstack apply output for specific errorsdstack run get <run-name> --json; consider spot vs on-demandWhen errors occur:
Core documentation:
Additional concepts:
Guides:
Accelerator-specific examples:
Full documentation: https://dstack.ai/llms-full.txt
© dstackai, MPL-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in skills/dstack of dstackai/dstack.
Open the folder on GitHubat commit 0d578c8
Dstack next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Dstack this skilldstackai/dstack | 2.3k | — | ~6.2k | Automated safety check: Warn | MPL-2.0 | |
| Paidf Orchestration SetupNVIDIA/skills | 3.5k | — | ~3.8k | Automated safety check: Warn | Apache-2.0 | |
| Helm Dev EnvironmentNVIDIA/OpenShell | 15k | — | ~4.9k | Automated safety check: Pass | Apache-2.0 | |
| Alibabacloud Ecs Sec Userspacealiyun/alibabacloud-ecs-troubleshoot-skills | 148 | — | ~2.6k | Automated safety check: Notes | Apache-2.0 | |
| TensorRT-LLM InferenceOrchestra-Research/AI-Research-SKILLs | 13k | 5 repos | ~1.3k | Automated safety check: Pass | MIT | |
| GPU Kubernetes Operationssickn33/agentic-awesome-skills | 47k | 2 repos | ~3.2k | Automated safety check: Pass | MIT |
NVIDIA/skills
Audit, prepare, and deploy PAIDF Orchestration on a Kubernetes GPU cluster - single-GPU H100/L40S hosts, managed Kubernetes, kubeadm, and similar.
NVIDIA/OpenShell
Start up, tear down, and configure the local Kubernetes development environment for OpenShell.
aliyun/alibabacloud-ecs-troubleshoot-skills
Linux 用户态安全入侵检测与取证工具,专为 AI Agent 设计。自动判断服务器是否被入侵, 提供完整证据链和可执行修复建议。51 个安全分析器覆盖进程/网络/认证/持久化/Rootkit/ 恶意软件/内存取证/容器逃逸等 12 类检测维度,10 个数据采集器全面采集系统状态, 映射 103+ MITRE ATT&CK 技术,支持 standalone/docker/k8s 三种部署模式。
Orchestra-Research/AI-Research-SKILLs
Optimizes and serves LLMs on NVIDIA GPUs with TensorRT-LLM, covering quantization, in-flight batching, multi-GPU parallelism and the trtllm-serve command.
sickn33/agentic-awesome-skills
Operate GPU-backed Kubernetes clusters for AI inference and training with scheduling, autoscaling, node health, MIG partitioning, and cost controls.
astronomer/agents
Builds, packs, and deploys compiled Airflow Go SDK bundles so the ExecutableCoordinator can run them.
dstackai/dstack
Use with the dstack skill for model-serving work when the image, serving command, resources, backend/fleet choice, or service behavior is not proven.
dstackai/dstack
Create and manage dstack presets: a toolkit that streamlines model inference optimization with agents, and a portable preset format.
Works with
Categories
dstack is an open-source control plane for GPU provisioning and orchestration across GPU clouds, Kubernetes, and on-prem clusters. Dstack is an agent skill from dstackai/dstack. dstack is an open-source control plane for GPU provisioning and orchestration across GPU clouds, Kubernetes, and on-prem clusters.
Dstack fits situations like: tasks that involve Container orchestration; tasks that involve GPU and accelerator computing.
Run `npx skills add dstackai/dstack --skill dstack -a claude-code`. Or copy the skill folder (skills/dstack in dstackai/dstack) into .claude/skills/dstack in your project. Claude Code loads it when a task matches its description.
Run `npx skills add dstackai/dstack --skill dstack -a codex`. Or copy the skill folder (skills/dstack in dstackai/dstack) into .agents/skills/dstack in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add dstackai/dstack --skill dstack -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/dstack, .gemini/skills/dstack, .github/skills/dstack and .opencode/skills/dstack in your project.
Going by SKILL.md and its folder, Dstack needs the command-line tools its instructions call (ssh and curl) and credentials named HF_TOKEN and HUGGING_FACE_HUB_TOKEN. Our summary lists: Docker.
SKILL.md names 1 domain. As links in the text: dstack.ai. This is read from the text; nothing was executed.
Our automated static check of SKILL.md flagged 3 warning(s): mentions a credentials file (ssh keys, cloud or package-manager tokens). Read the flagged lines before installing; the check is not a guarantee either way.
Dstack is published under the MPL-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 6.2k tokens (SKILL.md is roughly 25k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Dstack: Paidf Orchestration Setup (NVIDIA/skills, 3.5k stars), Helm Dev Environment (NVIDIA/OpenShell, 15k stars), Alibabacloud Ecs Sec Userspace (aliyun/alibabacloud-ecs-troubleshoot-skills, 148 stars) and TensorRT-LLM Inference (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
dstackai (a GitHub organization) maintains it in dstackai/dstack, which has 2,272 GitHub stars. The repository holds 3 skills in this directory. The repository was last updated on October 7, 2026.
Source: dstackai/dstack on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.