microsoft/airunway-aks-setup
skills/airunway-aks-setup/SKILL.md
Set up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment. WHEN: "setup AI Runway", "onboard AKS cluster", "install AI Runway", "airunway setup", "deploy model to AKS", "GPU inference on AKS", "KAITO setup on AKS", "run LLM on AKS", "vLLM on AKS", "set up model serving on AKS", "AI Runway controller".
$ install --global
skillsauthnpx skillsauth add microsoft/azure-skills airunway-aks-setupInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
3
Scanners Passed
9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
Skipped
OWASP Dep-Check
50%
Last scanned: Apr 23, 2026, 12:03 PM130.8s11 files scanned
SKILL.md
- name:
- airunway-aks-setup
- description:
- Set up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment. WHEN: \"setup AI Runway\", \"onboard AKS cluster\", \"install AI Runway\", \"airunway setup\", \"deploy model to AKS\", \"GPU inference on AKS\", \"KAITO setup on AKS\", \"run LLM on AKS\", \"vLLM on AKS\", \"set up model serving on AKS\", \"AI Runway controller\".
- license:
- MIT
- author:
- Microsoft
- version:
- 1.0.1
- argument-hint:
- [skip-to-step N]
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.
Related Skills
microsoft/skills/azure-app-onboard/scaffold
development
VerifiedTrustedCommunity
# Azure App Onboard Scaffold — IaC Generation + Self-Review Generate deployment-ready infrastructure code from an architecture plan, verify it with adversarial self-review, and bridge to validation — all without deploying. ## Quick Reference | Property | Value | |----------|-------| | Parent | [azure-app-onboard](../SKILL.md) | | Best for | Turning `prepare-plan.json` service list into Bicep templates with secure-by-default patterns | | Inputs | `prepare-plan.json` (services, naming, quotas),
1,320SKILL.mdUpdated Jul 26, 2026
microsoft/skills/azure-app-onboard/prepare
devops
VerifiedTrustedCommunity
# Prepare — Architecture Planning & Cost Estimation ## Quick Reference | Property | Value | |----------|-------| | Best for | Mapping app components to Azure services with cost estimation and quota validation | | Inputs | `prereq-output.json` + `context.json` from `.copilot-azure/sessions/{id}/` | | Outputs | `prepare-plan.json` written to session directory | | Parent | [azure-app-onboard](../SKILL.md) | ## When to Use This Skill Invoked by the `azure-app-onboard` orchestrator at Phase 2 whe
1,320SKILL.mdUpdated Jul 26, 2026
microsoft/skills/azure-app-onboard/deploy
testing
VerifiedTrustedCommunity
# Deploy — IaC Execution & Health Verification ## Quick Reference | Property | Value | |----------|-------| | Best for | Executing validated IaC against Azure, health-checking deployed resources | | Inputs | `prepare-plan.json` + `scaffold-manifest.json` from `.copilot-azure/sessions/{id}/` | | Outputs | `deploy-result.json` written to session directory | | Parent | [azure-app-onboard](../SKILL.md) | ## When to Use This Skill Invoked by the `azure-app-onboard` orchestrator at Phase 4 when `s
1,320SKILL.mdUpdated Jul 26, 2026
microsoft/azure-app-onboard
tools
VerifiedTrustedCommunity
End-to-end orchestrator: from a business idea, app idea, or existing app to running Azure deployment with cost estimates and pre-deploy approval. Analyzes your app, auto-detects the right Azure services, scaffolds infrastructure code, and deploys — tailored to your app, not a template. Handles moving existing apps to Azure without rewriting or with minimal changes. WHEN: bring your app to Azure, plan my app, cost to run, is my code ready to deploy, deploy my app to the cloud, deploy all my services, what Azure services do I need, plan my Azure deployment, deploy my new app to Azure, one-click deploy, I have an app and want it on Azure, migrate my app to Azure, help me get started, build an app, no code yet, starter project. DO NOT USE FOR: running azd up (use azure-deploy), optimizing existing costs (use azure-cost), code readiness checks only (use azure-app-onboard-prereq).
1,320SKILL.mdUpdated Jul 26, 2026