airunway-aks-setup — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited airunway-aks-setup (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
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.
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.
| 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 (baseline troubleshooting), optional aks-gpu-inference (existing GPU/inference Day-2 incidents only) |
Use this skill when the user wants to:
Keep initial GPU node-pool, provider, controller, and model setup in this skill. For an incident affecting an existing GPU or inference deployment, load optional-gpu-day2.md before deciding whether to offer the focused optional add-on.
This skill uses no MCP tools. All cluster operations are performed directly via kubectl and make.
skip-to-step N, start at step N; assume prior steps are complete| # | 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 / 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.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.