.github/plugins/azure-skills/skills/azure-kubernetes/SKILL.md
Plan, create, and configure production-ready Azure Kubernetes Service (AKS) clusters. Covers Day-0 checklist, SKU selection (Automatic vs Standard), networking options (private API server, Azure CNI Overlay, egress configuration), security, and operations (autoscaling, upgrade strategy, cost analysis). WHEN: create AKS environment, provision AKS environment, enable AKS observability, design AKS networking, choose AKS SKU, secure AKS, optimize AKS, rightsize AKS pod, AKS spot nodes, AKS cluster-autoscaler.
npx skillsauth add microsoft/azure-skills azure-kubernetesInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
AUTHORITATIVE GUIDANCE — MANDATORY COMPLIANCE
This skill produces a recommended AKS cluster configuration based on user requirements, distinguishing Day-0 decisions (networking, API server — hard to change later) from Day-1 features (can enable post-creation). See CLI reference for commands.
| Property | Value |
|----------|-------|
| Best for | AKS cluster planning and Day-0 decisions |
| MCP Tools | mcp_azure_mcp_aks |
| CLI | az aks create, az aks show, kubectl get, kubectl describe |
| Related skills | azure-diagnostics (troubleshooting AKS), azure-validate (readiness checks) |
Activate this skill when user wants to:
azure MCP server and select mcp_azure_mcp_aks first to discover the exact AKS-specific MCP tools surfaced by the client. Choose the smallest discovered AKS tool that fits the task, and fall back to Azure CLI (az aks) only when the needed functionality is not exposed through the AKS MCP surface.If the user is unsure, use safe defaults.
Pod IP Model (Key Day-0 decision):
Dataplane & Network Policy:
Egress:
Ingress:
DNS:
--node-osdisk-type Ephemeral) for faster node startupCriticalAddonsOnly)--zones 1 2 3)az aks stop/startDeep-dive scenarios — load only the relevant reference file:
| Scenario | Trigger Keywords | Reference | |----------|-----------------|-----------| | Pod Rightsizing | over-provisioned pods, CPU requests, memory requests, rightsize workloads | azure-aks-rightsizing.md | | VPA Setup | vertical pod autoscaler, VPA recommendations, VPA enable | azure-aks-vpa.md | | Cluster Autoscaler | idle nodes, CAS off, enable autoscaler, scale-down profile, node utilization | azure-aks-autoscaler.md | | Spot Node Pools | Spot VMs, Spot nodes, batch workloads, cheaper nodes | azure-aks-spot.md |
Disambiguation: If a prompt matches multiple rows (e.g., "cheaper nodes" could suggest both Spot and autoscaler), prefer the most specific match. If ambiguous, ask the user to clarify their intent before loading a reference file.
az account show / az account list so the agent can resolve context without exposing identifiers.| Tool | Purpose | Key Parameters |
|------|---------|----------------|
| mcp_azure_mcp_aks | AKS MCP entry point used to discover the exact AKS-specific tools exposed by the client | Discover the callable AKS tool first, then use that tool's parameters |
| Error / Symptom | Likely Cause | Remediation |
|-----------------|--------------|-------------|
| MCP tool call fails or times out | Invalid credentials, subscription, or AKS context | Verify az login, confirm the active subscription context with az account show, and check the target resource group without echoing subscription identifiers back to the user |
| Quota exceeded | Regional vCPU or resource limits | Request quota increase or select different region/VM SKU |
| Networking conflict (IP exhaustion) | Pod subnet too small for overlay/CNI | Re-plan IP ranges; may require cluster recreation (Day-0) |
| Workload Identity not working | Missing OIDC issuer or federated credential | Enable --enable-oidc-issuer --enable-workload-identity, configure federated identity |
development
# 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),
development
Assess and migrate cross-cloud workloads to Azure with reports and code conversion. Supports Lambda→Functions, Beanstalk/Heroku/App Engine→App Service, Fargate/Kubernetes/Cloud Run/Spring Boot→Container Apps. WHEN: migrate Lambda to Functions, AWS to Azure, migrate Beanstalk, migrate Heroku, migrate App Engine, Cloud Run migration, Fargate to ACA, ECS/Kubernetes/GKE/EKS to Container Apps, Spring Boot to Container Apps, cross-cloud migration.
development
Use when deploying an existing web application or API to an already-running Azure Kubernetes Service cluster. Detects the framework, generates a Dockerfile and Kubernetes manifests, validates against AKS Deployment Safeguards, and deploys with verification. WHEN: deploy app to AKS, deploy to existing AKS cluster, containerize app for Kubernetes, generate K8s manifests for Azure, set up CI/CD for AKS, my AKS deployment is failing safeguard checks, I have a Django/Express/Spring Boot app to run on AKS. DO NOT USE FOR: creating or provisioning an AKS cluster (use azure-kubernetes), assessing migration to AKS Automatic (use azure-kubernetes-automatic-readiness), or deploying to non-AKS targets like Web Apps, Container Apps, or Functions.
devops
Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file upload, calibrate grader, deploy fine-tuned model, evaluate fine-tuned model. DO NOT USE FOR: general model deployment without fine-tuning (use deploy-model), agent creation (use agents), prompt optimization without training (use prompt-optimizer).