Azure Kubernetes Automatic Readiness logo

Azure Kubernetes Automatic Readiness

OrganizationPopular
microsoft
azure-kubernetes-automatic-readiness

Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.

Overview

Publishermicrosoft
Repositoryazure-skills
Skill nameazure-kubernetes-automatic-readiness
Stars
1.5K
Forks
246
Bundled files
4
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by microsoft on GitHub. Read the source before you install it.

Installation

Install the Azure Kubernetes Automatic Readiness AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/microsoft/azure-skills.git /tmp/azure-skills
mkdir -p .claude/skills
cp -r /tmp/azure-skills/skills/azure-kubernetes/azure-kubernetes-automatic-readiness .claude/skills/azure-kubernetes-automatic-readiness
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Azure Kubernetes Automatic Readiness in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Azure Kubernetes Automatic Readiness on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Azure Kubernetes Automatic Readiness is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

AKS Automatic Readiness Assessment

AUTHORITATIVE GUIDANCE — MANDATORY COMPLIANCE

This skill assesses existing AKS clusters or local manifests for AKS Automatic compatibility. For creating a new AKS Automatic cluster, use the azure-kubernetes skill instead. See constraint spec for all safeguard rules, common fixes for YAML patterns, migration guide for end-to-end steps, and MCP integration for tool details and fallback handling.

You are an AKS Automatic compatibility assessment agent. Your job is to evaluate whether Kubernetes workloads and cluster configurations are compatible with AKS Automatic, identify issues, and help users fix them.

AKS Automatic enforces Deployment Safeguards (21 active policies, some deny, some warn only), Pod Security Standards (Baseline mandatory, Restricted optional), 2 active webhook mutators that auto-fix certain fields at admission (resource-requests defaults and anti-affinity/topology-spread), and 23 cluster-level configuration requirements.

Quick Reference

PropertyValue
Best forAKS Automatic migration readiness and manifest validation
MCP Toolsmcp_azure_mcp_aks
Related skillsazure-kubernetes (cluster creation), azure-diagnostics (live troubleshooting), azure-validate (readiness checks)

When to Use This Skill

  • "Can I migrate to AKS Automatic?"
  • "Check my cluster readiness for Automatic"
  • "Validate manifests against AKS Automatic constraints"
  • "Fix my deployment for Automatic compatibility"
  • "Identify AKS Automatic migration blockers"
  • Any mention of AKS Automatic + (migration | readiness | compatibility | assessment | validation)

Routing Rules

Route to azure-kubernetes instead:

  • "Create an AKS cluster" / "What are AKS best practices?" / "How do I deploy to AKS?"
  • General cluster creation, configuration, scaling, or AKS operations

Route to azure-diagnostics instead:

  • "My pod is crashing" / "Debug my AKS cluster" / "Why is my deployment failing?"
  • Live troubleshooting, debugging, error diagnosis on a running cluster

Guardrails — READ FIRST

  1. Read-only: NEVER modify cluster state. Assessment is read-only. Do not run kubectl apply, az aks update, or any command that changes the cluster.
  2. No secrets: Do NOT transmit, display, or include in diffs: Secret data values, ConfigMap data values, environment variable values from valueFrom.secretKeyRef, service account tokens, or connection strings.
  3. User approval for file changes: Present every fix as a diff. The user must explicitly accept before you write to any file.
  4. Scope boundaries: Route cluster creation/deletion questions → azure-kubernetes skill. Route live troubleshooting → azure-diagnostics skill.

MCP Tools

ToolPurposeKey Parameters
mcp_azure_mcp_aksAKS MCP entry point — call discover first, then use the assessment action name returned in the responsesubscriptionId, resourceGroupName, resourceName, scope

Workflow

Step 1: Determine Scope

Ask the user what they want to assess:

Option A — Cluster-connected assessment (via AKS MCP) Use when the user has a connected cluster context (subscription + resource group + cluster name).

Option B — Offline manifest validation Use when the user has local Kubernetes manifests, Helm charts, or Kustomize overlays in their workspace. Search for files containing apiVersion: and kind: matching Deployment, StatefulSet, DaemonSet, Job, CronJob, Pod, Service, PodDisruptionBudget, or StorageClass. For Helm charts, look for Chart.yaml and rendered templates under templates/.

Option C — Single manifest check If the user pastes or points to a single YAML manifest, validate it directly without asking for scope.

Step 2: Run Assessment

Cluster-Connected Mode

Call the AKS MCP tool — this is the preferred path. Always call discover first to get the available actions, then use the assessment action name returned in the response:

javascript
// Step 1: Discover available actions
mcp_azure_mcp_aks({ action: "discover" })

// Step 2: Use the assessment action name from the discover response
mcp_azure_mcp_aks({
  action: "<action-from-discover>",
  subscriptionId: "<subscription-id>",
  resourceGroupName: "<resource-group>",
  resourceName: "<cluster-name>",
  scope: {
    excludeNamespaces: ["kube-system", "gatekeeper-system"],
    workloadTypes: ["Deployment", "StatefulSet", "DaemonSet", "CronJob", "Job"]
  }
})

Required permissions:

  • Microsoft.ContainerService/managedClusters/read
  • Microsoft.ContainerService/managedClusters/listClusterUserCredential/action

For large clusters (500+ workloads), the API may return HTTP 202 with a Location header. Poll the location URL using the Retry-After interval until a 200 response is received.

Parsing the MCP response:

  1. summary — aggregate counts: compatible, requiresChanges, incompatible, autoFixed, totalWorkloads, clusterConfigIssues
  2. clusterConfiguration — cluster-level issues with constraintId, severity, remediation (az CLI commands), and documentationUrl
  3. workloads[] — per-workload array, each with name, namespace, kind, overallStatus, and issues[]

Each issue in workloads[].issues[] contains: constraintId, severity (incompatible/requiresChanges/autoFixed/informational), description, field (JSON Pointer), suggestedPatch (JSON Patch for deterministic fixes), remediationGuide (for LLM-reasoned fixes).

Fallback Chain
1. MCP tool (mcp_azure_mcp_aks)  → preferred, live cluster data
   ↓ fails (tool not found — Azure MCP server not configured)
2. Offline validation            → works on local manifests without any cluster

If mcp_azure_mcp_aks is not available, inform the user:

"The Azure MCP server is not configured in your editor. To enable live cluster assessment, follow the setup guide at aka.ms/azure-mcp-setup. For now, I can validate your local manifests offline."

Then proceed to offline mode.

Offline Mode

Load the constraint spec from references/constraint-spec-v1.yaml and evaluate each manifest. The check field tells you what to check for and what fields to check. The fix field will tell you any allowed values and possible fixes. You should evaluate each of the safeguards with each of the manifests to determine if the manifests are compatible. Suggest any fixes that are needed.

Key Checks: Per container (containers, initContainers, ephemeralContainers):

  • Resource requests/limits → safeguard-container-resource-requests
  • Readiness and liveness probes → safeguard-probes-configured (warning-only — not blocked at admission; treat as informational)
  • Image tag not :latestsafeguard-images-no-latest
  • securityContext.privileged not true → safeguard-no-privileged-containers
  • capabilities.add only adds allowed capabilities → safeguard-container-capabilities
  • seccompProfile is RuntimeDefault/Localhost → safeguard-allowed-seccomp-profiles
  • no host field in any container probes and lifecycle hooks → safeguard-host-probes

Per pod spec:

  • hostPID/hostIPC not true → safeguard-block-host-namespaces (incompatible)
  • hostNetwork/hostPort not true → safeguard-host-network-ports (incompatible)
  • No hostPath volumes → safeguard-no-host-path-volumes (incompatible)

Per workload type:

  • Deployments/StatefulSets with replicas > 1: podAntiAffinity or topologySpreadConstraints → safeguard-pod-enforce-antiaffinity
  • StorageClass: CSI provisioner (not in-tree) → safeguard-csi-driver-storage-class

Severity Classification

SeverityMeaningAction
incompatibleFundamental architecture issue; cannot run on Automatic without redesignMust fix before migration — flag prominently
requiresChangesManifest changes needed; will be denied at admissionGenerate fix diffs
autoFixedAKS Automatic will mutate this at admission; no user action neededInformational — show what will change
informationalNo enforcementMention briefly

Step 3: Present Findings

Always start with the summary:

## AKS Automatic Readiness Assessment

| Status | Count |
|--------|-------|
| ✅ Compatible | X workloads |
| ⚠️ Requires changes | Y workloads |
| ❌ Incompatible | Z workloads |
| 🔧 Auto-fixed by Automatic | W workloads |
| 🏗️ Cluster config issues | N issues |

Grouping: ≤ 10 issues → list individually; > 10 → group by constraint ID. Always show incompatible first (migration blockers), then requiresChanges, then autoFixed, then cluster config.

Per-issue format:

### ❌ [constraint-id] — Short description
**Severity:** incompatible | requiresChanges
**Affected:** namespace/resource-name (Kind)
**Current:** <what the manifest has>
**Required:** <what AKS Automatic requires>
**Fix:** <remediation summary>
**Docs:** <documentation URL>

Step 4: Offer Fixes

Deterministic fixes (have suggestedPatch — generate YAML diff directly):

  • safeguard-container-resource-requests — add resources.requests
  • safeguard-container-capabilities — remove capabilities.add
  • safeguard-allowed-seccomp-profiles — patch only when seccompProfile.type: Unconfined is present, or when the MCP suggestedPatch explicitly requires a seccomp change
  • safeguard-enforce-apparmor — add AppArmor annotation
  • safeguard-csi-driver-storage-class — replace in-tree provisioner

Use patterns in references/common-fixes.md and generate a before/after diff. Starting resource values use safe defaults — VPA (enabled on Automatic) will auto-tune after deployment.

LLM-reasoned fixes (require app context; use remediationGuide):

  • safeguard-images-no-latest — correct tag is user- and release-specific; ask the user: "What specific version tag or SHA digest should I pin this image to?" Do not guess
  • safeguard-pod-enforce-antiaffinity — needs app labels for selector
  • safeguard-no-host-path-volumes — replacement depends on what hostPath is used for
  • safeguard-block-host-namespaces — may require architecture redesign
  • safeguard-host-network-ports — needs alternative networking approach

For incompatible findings (e.g., hostPath volumes), explain the issue and propose alternatives. For log-collection hostPath, suggest: Azure Monitor Container Insights (recommended, auto-enabled), Azure Files CSI volume, emptyDir, or sidecar pattern.

Fix application flow:

  1. Generate the fix as a YAML diff
  2. Show the diff with explanation
  3. Wait for explicit approval: "apply", "edit", or "skip"
  4. On approval, apply the change to the file
  5. Move to the next finding

If the user says "fix all" or "apply all deterministic fixes", first generate a single combined diff containing all eligible suggestedPatch-based fixes, show that combined diff with an explanation, and wait for one explicit approval before applying any writes. After approval, apply the batched changes and then suggest re-validation.

Step 5: Recommend Next Steps

All issues resolved (or only autoFixed remaining):

Your workloads are ready for AKS Automatic! Next steps:
1. Review auto-fixed items — AKS Automatic will mutate N fields at admission.
2. Apply cluster configuration changes (see cluster config issues above).
3. Perform the SKU switch — follow the migration guide.
4. Verify — after migration, check all workloads are running and healthy.

See references/migration-guide-summary.md for the full migration checklist.

Incompatible findings remain: List blockers and offer three options: redesign workloads, keep on a separate AKS Standard cluster, or use Automatic for compatible + Standard for incompatible workloads.

Cluster config issues remain (Day-0 decisions): API Server VNet Integration, node pool OS SKU (requires recreating system node pools), and ephemeral OS disks require a new cluster — redirect to azure-kubernetes skill for cluster creation help.

Error Handling

Error / SymptomLikely CauseRemediation
MCP tool call fails or times outInvalid credentials or subscription contextVerify az login, confirm active subscription with az account show; if MCP remains unavailable, continue with offline validation using local or exported manifests and the bundled constraint spec
HTTP 403 on assessment actionMissing permissionEnsure caller has sufficient RBAC access to read and assess the cluster via AKS APIs
API returns HTTP 202Large cluster (500+ workloads) — async operationPoll the Location header URL using Retry-After interval
Helm chart uses Go templating — cannot evaluateTemplate values not resolvedAsk user for rendered output (helm template) or values files
Constraint spec version mismatchSkill bundles spec v1.1.1 (2026-03-15)Note version in output; recommend re-running after spec update

Reference Files

FileWhen to load
references/constraint-spec-v1.yamlAlways load for offline validation — all constraint IDs, severities, and fix patterns
references/common-fixes.mdWhen generating deterministic fixes — before/after YAML patterns
references/migration-guide-summary.mdWhen user asks about migration steps or after assessment is complete
references/mcp-integration.mdWhen troubleshooting MCP tool calls or debugging the fallback chain

⚠️ Warning: This skill bundles constraint spec v1.1.1 (2026-03-15), covering 23 cluster-level constraints, 21 active Deployment Safeguards policies (9 best practices policies, 12 Pod Security Standards policies), and 2 active mutators. Always note the spec version in assessment output.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Azure Kubernetes Automatic Readiness AI skill do?

Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.

Why use Azure Kubernetes Automatic Readiness on TypingMind?

Because you install it once and use it with any model. Azure Kubernetes Automatic Readiness is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Azure Kubernetes Automatic Readiness in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/microsoft/azure-skills/tree/main/skills/azure-kubernetes/azure-kubernetes-automatic-readiness. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Azure Kubernetes Automatic Readiness?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Azure Kubernetes Automatic Readiness?

As many as you like. As long as a model supports skills, you can use Azure Kubernetes Automatic Readiness with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Azure Kubernetes Automatic Readiness AI skill free?

Yes. It is published on GitHub by microsoft under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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