airunway-aks-setup

Takes a Kubernetes cluster you already have on Azure and walks it step by step — checking the hardware, installing the pieces, picking a serving engine — to the point where it can run an AI model.

WHEN TO USE

SCOPE

USE FOR

Install the AI Runway controller and its Kubernetes extensions onto an existing AKS cluster.
Assess your GPU nodes for compatibility before deploying a model.
Pick and install an inference provider such as KAITO, Dynamo, or KubeRay.
Deploy your first AI model to AKS and verify it is serving requests.
Resume a partly finished AI Runway setup from a specific step.

NOT FOR

Provisioning a brand-new AKS cluster; use azure-kubernetes for that first.

This is a saved copy of the skill's SKILL.md instructions. It may differ from the upstream file or the version you install.

In this document (8)

AI Runway AKS Setup

This skill walks users from a bare Kubernetes cluster to a running AI model deployment. Follow each step in sequence unless the user provides skip-to-step N to resume from a specific phase.

Cost awareness: GPU node pools incur significant compute charges (A100-80GB can cost $3–5+/hr). Confirm the user understands cost implications before provisioning GPU resources.

Prerequisites

This skill assumes an AKS cluster already exists. If the user does not have a cluster, hand off to the azure-kubernetes skill first to provision one (with a GPU node pool unless CPU-only inference is acceptable), then return here.

Quick Reference

Property	Value
Best for	End-to-end AI Runway onboarding on AKS
CLI tools	`kubectl`, `make`, `curl`
MCP tools	None
Related skills	`azure-kubernetes` (cluster setup), `azure-diagnostics` (troubleshooting)

When to Use This Skill

Use this skill when the user wants to:

Set up AI Runway on an existing AKS cluster from scratch
Install the AI Runway controller and CRDs
Assess GPU hardware compatibility for model deployment
Choose and install an inference provider (KAITO, Dynamo, KubeRay)
Deploy their first AI model to AKS via AI Runway
Resume a partially-complete AI Runway setup from a specific step

MCP Tools

This skill uses no MCP tools. All cluster operations are performed directly via kubectl and make.

Rules

Execute steps in sequence — load the reference for each step as you reach it
Report cluster state at each step: ✓ healthy, ✗ missing/failed
Ask for user confirmation before any install or deployment action
If a step is already complete, report status and skip to the next step
If the user provides skip-to-step N, start at step N; assume prior steps are complete

Steps

#	Step	Reference
1	Cluster Verification — context check, node inventory, GPU detection	step-1-verify.md
2	Controller Installation — CRD + controller deployment	step-2-controller.md
3	GPU Assessment — detect GPU models, flag dtype/attention constraints	step-3-gpu.md
4	Provider Setup — recommend and install inference provider	step-4-provider.md
5	First Deployment — pick a model, deploy, verify Ready	step-5-deploy.md
6	Summary — recap, smoke test, next steps	step-6-summary.md

Error Handling

Error / Symptom	Likely Cause	Remediation
No kubeconfig context	Not connected to a cluster	Run `az aks get-credentials` or equivalent
Controller in CrashLoopBackOff	Config or RBAC issue	`kubectl logs -n airunway-system -l control-plane=controller-manager --previous`
Provider not ready	Image pull or RBAC issue	`kubectl logs <pod-name> -n <namespace>` for the provider pod
ModelDeployment stuck in Pending	GPU scheduling failure or provider not ready	`kubectl describe modeldeployment <name> -n <namespace>` events
`bfloat16` errors at inference	T4 or V100 lacks bfloat16 support	Add `--dtype float16` to serving args

For full error handling and rollback procedures, see troubleshooting.md.

This skill ships inside the Azure Skills Plugin. Listed package contents include hooks and an MCP server. Installed components and skill versions may vary by AI tool.

Run this outside your AI tool, using Node.js 22.20.0 or newer. Skills CLI 1.7.2 skips skill selection; prompts cancel installation without an interactive terminal. If choosing Project when asked about installation scope, open your project's folder before running this.

Install checklist

Check selected tools if a picker appears; Claude Code may be unchecked under Additional agents. ↑/↓ moves, Space toggles selections, Enter confirms.
Without a tool picker, check Installing to: for your intended tool. Stop if it names a different tool.
If asked, choose Symlink (Recommended) for shared files or Copy to all agents for separate copies; review the summary before confirming installation.
Install the find-skills skill? defaults to Yes, adding another skill for all projects. Choose No if you don't want that extra installation.

Skills CLI 1.7.2 sends install data to skills.sh by default, including the source, skill names, target tools and file details; GitHub sources are reported only when GitHub confirms the repository is public, while other remote sources can also be reported. Confirmed-public GitHub sources also trigger a security-audit request. Before installing, set the environment variable DO_NOT_TRACK=1 or DISABLE_TELEMETRY=1 to disable both.

npx skills add microsoft/azure-skills --skill "airunway-aks-setup" --full-depth

After installing for Claude Code, open it in the installation project if you chose Project; Global makes the skill available across projects on this computer. Once Claude Code loads the skill, its description helps Claude decide when to use it.

If installation created a top-level skills folder that did not exist when your Claude Code session started, run /reload-skills in that session. Run it again after later changes in that folder.

What's inside

Files included with this skill.

View on GitHub

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AI Runway AKS Setup

Prerequisites

Quick Reference

When to Use This Skill

MCP Tools

Rules

Steps

Error Handling

Account or credentials required

What's inside

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