What does this skill do, and when should you use it?
This Agent Skill provides configuration guidance for Azure Kubernetes Service (AKS) based on user requirements. It covers SKU selection, networking, identity and security, observability, upgrades, node pools, reliability, and cost controls. It separates difficult-to-change Day-0 decisions from features that can be enabled after creation. It fits teams planning or configuring production AKS environments, while execution depends on Azure MCP, Azure CLI, kubectl, and access to the target environment.
Collects requirements for environment type, region, scale, networking, security, upgrades, observability, and cost; discovers AKS-specific tools through mcp_azure_mcp_aks first; falls back to az aks and kubectl commands when appropriate; evaluates AKS Automatic versus Standard; produces an AKS configuration recommendation with rationale for networking, API server access, identity, secrets, monitoring, upgrades, node pools, reliability, and cost; and loads the relevant rightsizing, VPA, Cluster Autoscaler, or Spot-node reference for matching scenarios.
- A platform engineer planning a production AKS cluster needs guidance on SKU, regions, availability zones, and node VM sizes.
- A cloud architect designing AKS networking needs to choose the Pod IP model, API server access, ingress, and egress approach.
- A security team configuring AKS needs Microsoft Entra ID, Workload Identity, Key Vault, Azure Policy, and signed-image controls.
- An operations team defining AKS operations needs observability, maintenance windows, automatic upgrades, and staged Fleet upgrades.
- A FinOps team optimizing AKS needs advice on node pools, Spot nodes, autoscaling, and dev/test cluster stop-start controls.
How do you install this skill?
- Before executing az aks, kubectl, or role-assignment commands, explicitly confirm the target subscription, resource group, cluster, and change scope, and establish a verifiable rollback plan.
- Validate the AKS SKU, networking, upgrade, VPA/NAP, encryption, and cost recommendations against the actual region, version, quotas, and existing-cluster state; documented defaults should not be treated as production approval.
- Confirm MCP permissions and what cluster metadata may be transmitted in the actual client; the supplied materials do not establish mainland-China reachability or an end-to-end Chinese-language experience.
- Shell / CLI
- Network access
- Local filesystem
- MCP Server
Azure CLIkubectlAzure MCP server
Run npx skills add microsoft/skills in the project and select the required skill in the installation wizard. Skills are installed to the chosen agent directory, such as .github/skills/ for GitHub Copilot, and can be symlinked for multiple agents. The source does not document a standalone command for installing only .github/plugins/azure-skills/skills/azure-kubernetes/.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/microsoft/skills.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/.github/plugins/azure-skills/skills/azure-kubernetes" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
After installation, make a natural-language AKS planning request, for example: Plan a production AKS cluster across three availability zones and explain the SKU, networking, identity, observability, upgrade, and cost choices. Provide requirements for Day-0 networking and API server decisions; for uncertain Day-1 features, allow the skill to propose conservative options and tradeoffs.
What are this skill's strengths and limitations?
- Covers the main AKS production-planning areas, including networking, security, observability, upgrades, reliability, and cost.
- Explicitly highlights Day-0 networking and API server decisions and requires rationale for configuration choices.
- Includes decision logic for AKS Automatic versus Standard and deep-dive triggers for rightsizing, VPA, autoscaling, and Spot nodes.
- Includes guardrails against requesting secrets, exposing subscription IDs, and promising zero downtime.
- Requires Azure MCP, Azure CLI, kubectl, and an authenticated Azure context; the source does not document installation steps for these dependencies.
- Primarily produces configuration recommendations and decision guidance; the source does not guarantee that it will create or deploy a cluster.
- Some commands and referenced details are stored in additional files not included in the supplied material.
- The README labels the repository as work in progress, so the skill and its tests may continue to change.
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| Azure AKS Production Planner this page ✓ Microsoft · Official | 54 · Use with care | ★ 2.7k | 3mo ago | MIT |
| GKE Cluster Autoscaler Guide ✓ Google · Official | 52 · Use with care | ★ 21k | 11d ago | Apache-2.0 |
| Azure Production Diagnostics ✓ Microsoft · Official | 52 · Use with care | ★ 2.7k | 3mo ago | MIT |
| Google Cloud Performance Advisor ✓ Google · Official | 50 · Use with care | ★ 21k | 11d ago | Apache-2.0 |
| GKE Production Readiness Review ✓ Google · Official | 49 · Use with care | ★ 21k | 11d ago | Apache-2.0 |
The skill explicitly compares AKS Automatic and AKS Standard. Automatic is the default for a curated experience with preconfigured security, reliability, and performance practices; Standard is for teams needing broader environment control and willing to manage the additional overhead.
How did FollowSkills review this skill?
The skill explicitly forbids requesting or outputting secrets, discourages static credentials, selects the smallest discovered tool, and asks for clarification on ambiguous Day-0 decisions. However, it also provides commands that can create, update, delete clusters, or change role assignments without consistently requiring per-action confirmation, and it lacks complete data-flow disclosure, rollback coverage, and execution isolation, so points are deducted.
The workflow, required inputs, error table, MCP discovery process, and offline fallback are reasonably detailed and provide some diagnosable failure feedback. Static materials contain no dedicated reproduction of the key paths, however, and several commands, version statements, and reference assumptions require environment validation; the score is also constrained by the static calibration cap.
The audience, trigger phrases, inputs, Day-0/Day-1 distinction, and routing to related skills are fairly clear. Non-fit boundaries are incomplete, the default toward AKS Automatic is broad, and there is no evidence addressing Chinese-language interaction or mainland-China network reachability, so points are deducted for limited environment-fit evidence.
The documentation has clear progressive sections covering quick reference, triggers, workflow, guardrails, error handling, on-demand references, MIT licensing, and version metadata. It lacks a clear changelog, named maintenance responsibility, and update path, while some behavior depends on runtime tools or external documentation, so it does not receive full marks.
For AKS planning, networking and security decisions, and Day-0 checklists, the material can produce directly useful recommendations with meaningful marginal value. Static review cannot verify command or recommendation correctness, representative outputs are not tested, and several recommendations require human review, so the score remains below the static maximum.
Versioned constraint material, explicit CLI/MCP call shapes, and several official documentation links provide useful audit trails. There is no independent execution evidence, key-path test coverage, or meaningful cross-source corroboration for this skill, and fact-versus-inference separation is inconsistent, so points are deducted.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →