Deploy Controller
ai-runway/airunway
Interactively build, push or load, and deploy an airunway component (controller or any provider) to the cluster
Select, validate, patch, and deploy existing NVIDIA Dynamo Kubernetes recipes.
Install Dynamo Recipe Runner
$ npx skills add NVIDIA/skills --skill dynamo-recipe-runner -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/skills dynamo-recipe-runner --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/NVIDIA/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/dynamo-recipe-runner .claude/skills/dynamo-recipe-runner && 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 "dynamo-recipe-runner" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/dynamo-recipe-runner into .claude/skills/dynamo-recipe-runner/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dynamo-recipe-runner", 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/NVIDIA/skills/tree/main/skills/dynamo-recipe-runnerType 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 NVIDIA/skills --skill dynamo-recipe-runner -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/skills dynamo-recipe-runner --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/dynamo-recipe-runner .agents/skills/dynamo-recipe-runner && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "dynamo-recipe-runner" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/dynamo-recipe-runner into .agents/skills/dynamo-recipe-runner/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dynamo-recipe-runner", 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 NVIDIA/skills --skill dynamo-recipe-runner -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/skills dynamo-recipe-runner --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/dynamo-recipe-runner .cursor/skills/dynamo-recipe-runner && 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 "dynamo-recipe-runner" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/dynamo-recipe-runner into .cursor/skills/dynamo-recipe-runner/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dynamo-recipe-runner", 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/NVIDIA/skills.git --path skills/dynamo-recipe-runner--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 NVIDIA/skills --skill dynamo-recipe-runner -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/skills dynamo-recipe-runner --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/dynamo-recipe-runner .gemini/skills/dynamo-recipe-runner && 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 "dynamo-recipe-runner" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/dynamo-recipe-runner into .gemini/skills/dynamo-recipe-runner/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dynamo-recipe-runner", 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 NVIDIA/skills dynamo-recipe-runnerInstalls 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 NVIDIA/skills --skill dynamo-recipe-runner -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/dynamo-recipe-runner .github/skills/dynamo-recipe-runner && 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 "dynamo-recipe-runner" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/dynamo-recipe-runner into .github/skills/dynamo-recipe-runner/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dynamo-recipe-runner", 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 NVIDIA/skills --skill dynamo-recipe-runner -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install NVIDIA/skills dynamo-recipe-runner --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/dynamo-recipe-runner .opencode/skills/dynamo-recipe-runner && 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 "dynamo-recipe-runner" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/dynamo-recipe-runner into .opencode/skills/dynamo-recipe-runner/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dynamo-recipe-runner", 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.
Facts
- Skill name
dynamo-recipe-runner- GitHub stars
- 3.5k
- Token cost
- ~1.8k tokens
- SKILL.md length
- 700 words
- Files
- 7 (incl. scripts, references)
- Skills in repo
- 380
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Select, validate, patch, and deploy existing NVIDIA Dynamo Kubernetes recipes.
- Works in 6 steps: Preflight → Select The Recipe → Inspect And Validate → …
- Model/backend/GPU/deployment-mode recipe bring-up
- SKILL.md covers Purpose, Prerequisites, Required Inputs and Instructions, plus 7 more sections
- Runs Python scripts from its folder; calls kubectl, python3 and git
What it does
Dynamo Recipe Runner is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Select, validate, patch, and deploy existing NVIDIA Dynamo Kubernetes recipes. Use for model/backend/GPU/deployment-mode recipe bring-up; use router-starter for router-only mode work and troubleshoot for broken deployments.
Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including scripts and reference files (for example `BENCHMARK.md`, `evals/evals.json` and `references/k8s-recipe-workflow.md`).
It sits in DevOps & Cloud, covering Deployment and Container orchestration. It works with NVIDIA AI Platform and Kubernetes. The repository describes itself as: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. The licence is Apache-2.0.
When your agent uses it
- Model/backend/GPU/deployment-mode recipe bring-up
- Use router-starter for router-only mode work and troubleshoot for broken deployments
Example prompts
- “/dynamo-recipe-runner”
Requirements
- Python 3
Workflow steps
6 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 67a13c0. It shows what the files ask for, not the result of running them.
Tool permissions
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.
Runs code
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
kubectlpython3gitcurl
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use kubectl, git and curl, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Dynamo Recipe Runner loads about 1.8k tokens when it runs, and up to ~2.6k if it reads all its reference files. Until then it costs about 61 tokens; SKILL.md has 700 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.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated 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); the scripts in this folder are not scanned.
SKILL.md
The full file from NVIDIA/skills at commit 67a13c0, republished under its Apache-2.0 licence (© NVIDIA). 700 words, ~1,834 tokens.
Download SKILL.mdSave it as
.claude/skills/dynamo-recipe-runner/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.- name
- dynamo-recipe-runner
- description
- Select, validate, patch, and deploy existing NVIDIA Dynamo Kubernetes recipes. Use for model/backend/GPU/deployment-mode recipe bring-up; use router-starter for router-only mode work and troubleshoot for broken deployments.
- license
- Apache-2.0
- metadata.author
- Dan Gil <dagil@nvidia.com>
- metadata.tags
- dynamo, kubernetes, recipes, bring-up
- metadata.permissions
- file_read, network, kubectl_exec
Dynamo Recipe Runner
<!--
SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
SPDX-License-Identifier: CC-BY-4.0
-->Purpose
Get from user intent to a working Dynamo recipe endpoint with minimal back and
forth. Do not create new guide content. Operate on the existing recipes/
tree, patch the smallest necessary set of manifests, deploy when the user has
cluster access, and prove success with an OpenAI-compatible smoke request.
Prerequisites
- Python 3.10+ on the operator machine.
kubectlconfigured with a working cluster context.- Cluster has a default storage class for model-cache PVCs.
- Hugging Face token stored in a Kubernetes secret named
hf-token-secret(or equivalent) in the target namespace. - Read access to the
recipes/tree in the ai-dynamo/dynamo repository.
Required Inputs
Collect or infer these before changing manifests:
- recipe target: model, framework (
vllm,sglang,trtllm,tokenspeed), deployment mode, and GPU type/count - Kubernetes context and namespace
- Hugging Face secret name, usually
hf-token-secret - storage class for model cache PVCs
- runtime image tag if the recipe uses a placeholder or stale test image
- whether to run commands or only produce exact commands
If a required value is missing and cannot be inferred from the selected recipe, ask for only that value.
Instructions
1. Preflight
Run read-only checks first:
git status --short
python3 scripts/recipe_tool.py list --format table
kubectl config current-context
kubectl get storageclass
kubectl get nodes -o wide
kubectl get namespace "${NAMESPACE}"
kubectl get secret hf-token-secret -n "${NAMESPACE}"If kubectl is unavailable or the cluster is unreachable, continue by
selecting and validating the recipe, then return exact commands instead of
pretending the deployment ran.
2. Select The Recipe
Use the recipe matrix from recipes/README.md and the scanner:
python3 scripts/recipe_tool.py list \
--query qwen --framework vllm --mode disagg --format tablePrefer an exact existing recipe. Do not invent new manifests unless the user explicitly asks to author a new recipe.
3. Inspect And Validate
Read the selected recipe README, model-cache manifests, deploy.yaml, and
perf.yaml if present. Then run:
python3 scripts/recipe_tool.py validate \
recipes/<model>/<framework>/<mode>Resolve reported blockers before applying manifests: storage class, model cache PVC, image tag, HF token secret, GPU count, frontend service name, and router mode.
4. Patch Minimal Values
Patch only recipe-specific values needed for this run. Do not reformat whole YAML files. Common patches:
storageClassName- image repository/tag
- model path or model cache mount path
- GPU resource requests/limits
- frontend
DYN_ROUTER_MODE - namespace only when a manifest hardcodes it
Never write Hugging Face tokens into files or logs. Use Kubernetes secrets.
5. Deploy
Follow the selected recipe README when it differs from the default sequence. The default sequence is:
kubectl apply -f recipes/<model>/model-cache/ -n "${NAMESPACE}"
kubectl wait --for=condition=Complete job/model-download -n "${NAMESPACE}" --timeout=6000s
kubectl apply -f recipes/<model>/<framework>/<mode>/deploy.yaml -n "${NAMESPACE}"
kubectl get dynamographdeployment -n "${NAMESPACE}"
kubectl get pods -n "${NAMESPACE}" -o wideWait for the frontend and workers to be ready before testing.
6. Smoke Test
Port-forward the frontend service, then verify /v1/models and one chat
completion:
kubectl port-forward svc/<deployment-name>-frontend 8000:8000 -n "${NAMESPACE}"
curl http://127.0.0.1:8000/v1/modelsIf dynamo-router-starter is also installed, prefer its scripts/check_router_health.py
for the full OpenAI-compatible smoke test. If this fails, switch to
dynamo-troubleshoot.
Show full SKILL.md (294 more words)Show less
Available Scripts
| Script | Purpose | Arguments |
|---|---|---|
scripts/recipe_tool.py list | Enumerate available recipes, optionally filtered | --query, --framework, --mode, --format |
scripts/recipe_tool.py validate | Validate a recipe directory before apply | positional recipe path |
Invoke via the agentskills.io run_script() protocol:
run_script("scripts/recipe_tool.py", args=["list", "--framework", "sglang", "--format", "table"])
run_script("scripts/recipe_tool.py", args=["validate", "recipes/nemotron-3-super-fp8/sglang/agg"])Examples
List sglang recipes that fit a single 8xB200 node:
python3 scripts/recipe_tool.py list --framework sglang --format tableValidate a specific recipe and resolve blockers before applying:
python3 scripts/recipe_tool.py validate recipes/nemotron-3-super-fp8/sglang/aggEquivalent through the agent protocol:
run_script("scripts/recipe_tool.py", args=["validate", "recipes/nemotron-3-super-fp8/sglang/agg"])Output Contract
Return:
- selected recipe path and why it was selected
- exact values patched
- commands run or commands to run
- endpoint and smoke-test result
- unresolved blockers, if any
- next troubleshooting step when deployment does not become healthy
Limitations
- Operates on the existing
recipes/tree only. Does not author new manifests. - Cluster-mutating apply steps require
kubectlpermission to the target namespace. - Smoke-test depth is intentionally minimal; for full router/endpoint coverage use
dynamo-router-starter. - Multi-node disagg transport correctness is out of scope; use
dynamo-interconnect-checkafter deploy.
Troubleshooting
| Symptom | Likely cause | Next step |
|---|---|---|
kubectl cluster unreachable | Context not set or VPN down | Return exact commands instead of running them; resume when cluster is reachable |
validate reports missing storage class | Cluster has no default StorageClass | Patch storageClassName on the model-cache manifest before applying |
Model-cache job stuck Pending | PVC unbound or HF secret missing | Inspect PVC events; create or rename the HF secret to match the recipe |
Worker pods ImagePullBackOff | Stale image tag or missing pull secret | Patch the image tag; verify image pull secret in the namespace |
/v1/models 4xx/5xx after deploy | Frontend not ready or wrong service port | Wait for pods Ready; re-run port-forward; switch to dynamo-troubleshoot if it persists |
Benchmark
See BENCHMARK.md for the NVCARPS-EVAL performance report (auto-generated by the NVSkills CI pipeline). To refresh, re-run /nvskills-ci on an upstream PR touching this skill.
References
- Read
references/k8s-recipe-workflow.mdfor command templates and readiness checks. - Use
scripts/recipe_tool.pyfor recipe discovery and lightweight validation.
© NVIDIA, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 6 other files (scripts, references) in skills/dynamo-recipe-runner of NVIDIA/skills.
- SKILL.md
- BENCHMARK.md
- evals/evals.json
- references/k8s-recipe-workflow.md
- scripts/recipe_tool.py
- skill-card.md
- skill.oms.sig
Open the folder on GitHubat commit 67a13c0
Compare with similar skills
Dynamo Recipe Runner 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 |
|---|---|---|---|---|---|---|
| Dynamo Recipe Runner this skillNVIDIA/skills | 3.5k | — | ~1.8k | Automated safety check: Pass | Apache-2.0 | |
| Deploy Controllerai-runway/airunway | 101 | — | ~927 | Automated safety check: Pass | Apache-2.0 | |
| Model Serving Kubernetessickn33/agentic-awesome-skills | 47k | 1 repos | ~2.3k | Automated safety check: Pass | MIT | |
| Model Serving Kubernetesmajiayu000/claude-skill-registry | 666 | 1 repos | ~2.1k | Automated safety check: Pass | MIT | |
| LangBot Deployment Guidelangbot-app/LangBot | 18k | — | ~1.2k | Automated safety check: Notes | Apache-2.0 | |
| Openbkn Deployopenbkn-ai/bkn-foundry | 636 | — | ~1.9k | Automated safety check: Notes | Custom licence |
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Questions about Dynamo Recipe Runner
What does Dynamo Recipe Runner do?
Select, validate, patch, and deploy existing NVIDIA Dynamo Kubernetes recipes. Dynamo Recipe Runner is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Select, validate, patch, and deploy existing NVIDIA Dynamo Kubernetes recipes.
When should I use Dynamo Recipe Runner?
Dynamo Recipe Runner fits situations like: model/backend/GPU/deployment-mode recipe bring-up; use router-starter for router-only mode work and troubleshoot for broken deployments.
How do I install Dynamo Recipe Runner in Claude Code?
Run `npx skills add NVIDIA/skills --skill dynamo-recipe-runner -a claude-code`. Or copy the skill folder (skills/dynamo-recipe-runner in NVIDIA/skills) into .claude/skills/dynamo-recipe-runner in your project. Claude Code loads it when a task matches its description.
How do I install Dynamo Recipe Runner in Codex?
Run `npx skills add NVIDIA/skills --skill dynamo-recipe-runner -a codex`. Or copy the skill folder (skills/dynamo-recipe-runner in NVIDIA/skills) into .agents/skills/dynamo-recipe-runner in your project. Codex loads it when a task matches its description.
Can I use Dynamo Recipe Runner in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA/skills --skill dynamo-recipe-runner -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/dynamo-recipe-runner, .gemini/skills/dynamo-recipe-runner, .github/skills/dynamo-recipe-runner and .opencode/skills/dynamo-recipe-runner in your project.
What does Dynamo Recipe Runner need to run?
Going by SKILL.md and its folder, Dynamo Recipe Runner needs Python for the scripts in its folder and the command-line tools its instructions call (kubectl, python3, git and curl). Our summary lists: Python 3.
Does Dynamo Recipe Runner access the network?
SKILL.md contains no URLs. Its commands use git and curl, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Dynamo Recipe Runner safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Dynamo Recipe Runner use?
Dynamo Recipe Runner is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Dynamo Recipe Runner use?
About 1.8k tokens (SKILL.md is roughly 7.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 726 tokens, read only when the agent opens those files.
What are the alternatives to Dynamo Recipe Runner?
Skills that share tags, products or a category with Dynamo Recipe Runner: Deploy Controller (ai-runway/airunway, 101 stars), Model Serving Kubernetes (sickn33/agentic-awesome-skills, 47k stars), Model Serving Kubernetes (majiayu000/claude-skill-registry, 666 stars) and LangBot Deployment Guide (langbot-app/LangBot, 18k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Dynamo Recipe Runner?
NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,539 GitHub stars. The repository holds 380 skills in this directory. The repository was last updated on October 7, 2026.
Source: NVIDIA/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.