fix: quote argument-hint YAML values so Copilot CLI ≥1.0.65 loads all skills (closes #5066)

.claude/commands/pr.md

@@ -2,7 +2,7 @@
 description: Format code, stage changes, and create a pull request
 allowed-tools:
   - Bash
-argument-hint: [base-branch]
+argument-hint: "[base-branch]"
 ---
 
 # Prepare and create pull request

docs/book/component-guide/orchestrators/kubernetes.md

@@ -6,6 +6,8 @@ description: Orchestrating your pipelines to run on Kubernetes clusters.
 
 Using the ZenML `kubernetes` integration, you can orchestrate and scale your ML pipelines on a [Kubernetes](https://kubernetes.io/) cluster without writing a single line of Kubernetes code.
 
+The orchestrator uses standard Kubernetes APIs and works on managed cloud clusters (EKS, GKE, AKS), enterprise platforms like Red Hat OpenShift and SUSE Rancher, and self-managed clusters on-premises or in your own cloud.
+
 This Kubernetes-native orchestrator is a minimalist, lightweight alternative to other distributed orchestrators like Airflow or Kubeflow.
 
 Overall, the Kubernetes orchestrator is quite similar to the Kubeflow orchestrator in that it runs each pipeline step in a separate Kubernetes pod. However, the orchestration of the different pods is not done by Kubeflow but by a separate master pod that orchestrates the step execution via topological sort.
@@ -20,15 +22,18 @@ This component is only meant to be used within the context of a [remote ZenML de
 
 You should use the Kubernetes orchestrator if:
 
+* you already have a Kubernetes cluster—managed cloud, Red Hat OpenShift, SUSE Rancher, or self-managed on-premises.
 * you're looking for a lightweight way of running your pipelines on Kubernetes.
 * you're not willing to maintain [Kubeflow Pipelines](kubeflow.md) on your Kubernetes cluster.
 * you're not interested in paying for managed solutions like [Vertex](vertex.md).
 
 ## How to deploy it
 
-The Kubernetes orchestrator requires a Kubernetes cluster in order to run. There are many ways to deploy a Kubernetes cluster using different cloud providers or on your custom infrastructure, and we can't possibly cover all of them, but you can check out our [our production guide](https://docs.zenml.io/user-guides/production-guide).
+The Kubernetes orchestrator requires a Kubernetes cluster (version 1.21 or higher recommended).
+
+There are many ways to deploy a Kubernetes cluster using different cloud providers or on your custom infrastructure, and we can't possibly cover all of them, but you can check out our [production guide](https://docs.zenml.io/user-guides/production-guide).
 
-If the above Kubernetes cluster is deployed remotely on the cloud, then another pre-requisite to use this orchestrator would be to deploy and connect to a [remote ZenML server](https://docs.zenml.io/getting-started/deploying-zenml/).
+If the Kubernetes cluster is deployed remotely, another prerequisite is to deploy and connect to a [remote ZenML server](https://docs.zenml.io/getting-started/deploying-zenml/).
 
 ## How to use it
 
@@ -42,7 +47,7 @@ To use the Kubernetes orchestrator, we need:
 * [Docker](https://www.docker.com) installed and running.
 * A [remote artifact store](../artifact-stores/README.md) as part of your stack.
 * A [remote container registry](../container-registries/README.md) as part of your stack.
-* A Kubernetes cluster [deployed](kubernetes.md#how-to-deploy-it)
+* A Kubernetes cluster [deployed](kubernetes.md#how-to-deploy-it) (version 1.21 or higher recommended)
 * [kubectl](https://kubernetes.io/docs/tasks/tools/#kubectl) installed and the name of the Kubernetes configuration context which points to the target cluster (i.e. run`kubectl config get-contexts` to see a list of available contexts) . This is optional (see below).
 
 {% hint style="info" %}

docs/book/getting-started/deploying-zenml/deploy-with-helm.md

@@ -6,13 +6,15 @@ description: Deploying ZenML in a Kubernetes cluster with Helm.
 
 If you wish to manually deploy and manage ZenML in a Kubernetes cluster of your choice, ZenML also includes a Helm chart among its available deployment options.
 
+The chart uses standard Kubernetes APIs and works on managed cloud clusters (EKS, GKE, AKS), enterprise platforms like Red Hat OpenShift and SUSE Rancher, and self-managed clusters on-premises or in your own cloud.
+
 You can find the chart on this [ArtifactHub repository](https://artifacthub.io/packages/helm/zenml/zenml), along with the templates, default values and instructions on how to install it. Read on to find detailed explanations on prerequisites, configuration, and deployment scenarios.
 
 ## Prerequisites
 
 You'll need the following:
 
-* A Kubernetes cluster
+* A Kubernetes cluster (version 1.21 or higher recommended)
 * Optional, but recommended: a MySQL-compatible database reachable from the Kubernetes cluster (e.g. one of the managed databases offered by Google Cloud, AWS, or Azure). A MySQL server version of 8.0 or higher is required
 * the [Kubernetes client](https://kubernetes.io/docs/tasks/tools/#kubectl) already installed on your machine and configured to access your cluster
 * [Helm](https://helm.sh/docs/intro/install/) installed on your machine

docs/book/getting-started/zenml-pro/deploy-control-plane-k8s.md

@@ -17,6 +17,8 @@ layout:
 
 This guide provides step-by-step instructions for deploying ZenML Pro on Kubernetes using Helm charts. All components run within your infrastructure with zero external dependencies.
 
+ZenML Pro deployments use standard Kubernetes APIs and work on managed cloud clusters (EKS, GKE, AKS), enterprise platforms like Red Hat OpenShift and SUSE Rancher, and self-managed clusters on-premises or in your own cloud.
+
 ## Deployment Architecture
 
 All components run entirely within your Kubernetes cluster and infrastructure:

docs/book/getting-started/zenml-pro/deploy-prerequisites.md

@@ -238,12 +238,14 @@ The Control Plane and Workspace Server components are completely independent fro
 
 | Infrastructure Type | Support Level |
 |---------------------|---------------|
-| Kubernetes (managed or self-hosted, 1.24+) | ✅ Officially supported and documented |
+| Kubernetes (1.24+) | ✅ Officially supported and documented |
 | Container-as-a-Service (ECS, Cloud Run, etc.) | ⚠️ Possible but not documented |
 | Serverless containers (Fargate, Lambda, etc.) | ⚠️ Possible but not documented |
 | Virtual machines with Docker (VMware, VirtualBox, etc.) | ⚠️ Possible but not documented |
 | Docker-compose | ✅ Can be provided upon request for local testing purposes |
 
+These Helm-based deployments use standard Kubernetes APIs and work on managed cloud clusters (EKS, GKE, AKS), enterprise platforms like Red Hat OpenShift and SUSE Rancher, and self-managed clusters on-premises or in your own cloud.
+
 {% hint style="warning" %}
 While ZenML Pro can theoretically run on any container orchestration platform, only Kubernetes deployments are officially fully supported and documented. If you require a different deployment target, please contact [cloud@zenml.io](mailto:cloud@zenml.io) to discuss your requirements.
 {% endhint %}

docs/book/getting-started/zenml-pro/deploy-workspace-k8s.md

@@ -17,6 +17,8 @@ layout:
 
 This guide provides step-by-step instructions for deploying ZenML Pro workspaces on Kubernetes using Helm and enrolling them in the ZenML Pro control plane.
 
+ZenML Pro workspace deployments use standard Kubernetes APIs and work on managed cloud clusters (EKS, GKE, AKS), enterprise platforms like Red Hat OpenShift and SUSE Rancher, and self-managed clusters on-premises or in your own cloud.
+
 
 ## Prerequisites