k8s-capi
Cluster API lifecycle management for provisioning, scaling, and upgrading Kubernetes clusters. Use when managing cluster infrastructure or multi-cluster operations.
Best use case
k8s-capi is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Cluster API lifecycle management for provisioning, scaling, and upgrading Kubernetes clusters. Use when managing cluster infrastructure or multi-cluster operations.
Teams using k8s-capi should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/k8s-capi/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How k8s-capi Compares
| Feature / Agent | k8s-capi | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Cluster API lifecycle management for provisioning, scaling, and upgrading Kubernetes clusters. Use when managing cluster infrastructure or multi-cluster operations.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Cluster API Lifecycle Management
Manage Kubernetes clusters using kubectl-mcp-server's Cluster API tools (11 tools).
## When to Apply
Use this skill when:
- User mentions: "Cluster API", "CAPI", "cluster lifecycle", "machine", "workload cluster"
- Operations: provisioning clusters, scaling nodes, upgrading Kubernetes versions
- Keywords: "provision cluster", "scale workers", "machine deployment", "cluster class"
## Priority Rules
| Priority | Rule | Impact | Tools |
|----------|------|--------|-------|
| 1 | Detect CAPI installation first | CRITICAL | `capi_detect_tool` |
| 2 | Check cluster phase before operations | HIGH | `capi_cluster_get_tool` |
| 3 | Monitor machines during scaling | HIGH | `capi_machines_list_tool` |
| 4 | Get kubeconfig after provisioning | MEDIUM | `capi_cluster_kubeconfig_tool` |
## Quick Reference
| Task | Tool | Example |
|------|------|---------|
| Detect CAPI | `capi_detect_tool` | `capi_detect_tool()` |
| List clusters | `capi_clusters_list_tool` | `capi_clusters_list_tool(namespace)` |
| Get cluster kubeconfig | `capi_cluster_kubeconfig_tool` | `capi_cluster_kubeconfig_tool(name, namespace)` |
| Scale workers | `capi_machinedeployment_scale_tool` | `capi_machinedeployment_scale_tool(name, namespace, replicas)` |
## Check Installation
```python
capi_detect_tool()
```
## List Clusters
```python
# List all CAPI clusters
capi_clusters_list_tool(namespace="default")
# Shows:
# - Cluster name
# - Phase (Provisioning, Provisioned, Deleting)
# - Infrastructure ready
# - Control plane ready
```
## Get Cluster Details
```python
capi_cluster_get_tool(name="my-cluster", namespace="default")
# Shows:
# - Spec (control plane, infrastructure)
# - Status (phase, conditions)
# - Network configuration
```
## Get Cluster Kubeconfig
```python
# Get kubeconfig for workload cluster
capi_cluster_kubeconfig_tool(name="my-cluster", namespace="default")
# Returns kubeconfig to access the cluster
```
## Machines
### List Machines
```python
capi_machines_list_tool(namespace="default")
# Shows:
# - Machine name
# - Cluster
# - Phase (Running, Provisioning, Failed)
# - Provider ID
# - Version
```
### Get Machine Details
```python
capi_machine_get_tool(name="my-cluster-md-0-xxx", namespace="default")
```
## Machine Deployments
### List Machine Deployments
```python
capi_machinedeployments_list_tool(namespace="default")
# Shows:
# - Deployment name
# - Cluster
# - Replicas (ready/total)
# - Version
```
### Scale Machine Deployment
```python
# Scale worker nodes
capi_machinedeployment_scale_tool(
name="my-cluster-md-0",
namespace="default",
replicas=5
)
```
## Machine Sets
```python
capi_machinesets_list_tool(namespace="default")
```
## Machine Health Checks
```python
capi_machinehealthchecks_list_tool(namespace="default")
# Health checks automatically remediate unhealthy machines
```
## Cluster Classes
```python
# List cluster templates
capi_clusterclasses_list_tool(namespace="default")
# ClusterClasses define reusable cluster configurations
```
## Create Cluster
```python
kubectl_apply(manifest="""
apiVersion: cluster.x-k8s.io/v1beta1
kind: Cluster
metadata:
name: my-cluster
namespace: default
spec:
clusterNetwork:
pods:
cidrBlocks:
- 192.168.0.0/16
services:
cidrBlocks:
- 10.96.0.0/12
controlPlaneRef:
apiVersion: controlplane.cluster.x-k8s.io/v1beta1
kind: KubeadmControlPlane
name: my-cluster-control-plane
infrastructureRef:
apiVersion: infrastructure.cluster.x-k8s.io/v1beta1
kind: AWSCluster
name: my-cluster
""")
```
## Create Machine Deployment
```python
kubectl_apply(manifest="""
apiVersion: cluster.x-k8s.io/v1beta1
kind: MachineDeployment
metadata:
name: my-cluster-md-0
namespace: default
spec:
clusterName: my-cluster
replicas: 3
selector:
matchLabels:
cluster.x-k8s.io/cluster-name: my-cluster
template:
spec:
clusterName: my-cluster
version: v1.28.0
bootstrap:
configRef:
apiVersion: bootstrap.cluster.x-k8s.io/v1beta1
kind: KubeadmConfigTemplate
name: my-cluster-md-0
infrastructureRef:
apiVersion: infrastructure.cluster.x-k8s.io/v1beta1
kind: AWSMachineTemplate
name: my-cluster-md-0
""")
```
## Cluster Lifecycle Workflows
### Provision New Cluster
```python
1. kubectl_apply(cluster_manifest)
2. capi_clusters_list_tool(namespace) # Wait for Provisioned
3. capi_cluster_kubeconfig_tool(name, namespace) # Get access
```
### Scale Workers
```python
1. capi_machinedeployments_list_tool(namespace)
2. capi_machinedeployment_scale_tool(name, namespace, replicas)
3. capi_machines_list_tool(namespace) # Monitor
```
### Upgrade Cluster
```python
1. # Update control plane version
2. # Update machine deployment version
3. capi_machines_list_tool(namespace) # Monitor rollout
```
## Troubleshooting
### Cluster Stuck Provisioning
```python
1. capi_cluster_get_tool(name, namespace) # Check conditions
2. capi_machines_list_tool(namespace) # Check machine status
3. get_events(namespace) # Check events
4. # Check infrastructure provider logs
```
### Machine Failed
```python
1. capi_machine_get_tool(name, namespace)
2. get_events(namespace)
3. # Common issues:
# - Cloud provider quota
# - Invalid machine template
# - Network issues
```
## Related Skills
- [k8s-multicluster](../k8s-multicluster/SKILL.md) - Multi-cluster operations
- [k8s-operations](../k8s-operations/SKILL.md) - kubectl operationsRelated Skills
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