
K8s
- 54 installs
- 6 repo stars
- Updated March 13, 2026
- alphaonedev/openclaw-graph
k8s is a Claude skill for orchestrating containerized applications with Kubernetes - deploying and scaling pods, services, and deployments, and managing cluster resources.
About
This skill orchestrates containerized applications with Kubernetes. A developer uses it to deploy and manage pods, services, and deployments via kubectl or YAML manifests, scale workloads, expose services, handle persistent storage, and manage secrets and config maps. It targets production cluster operations and microservices architectures.
- Deploys and manages pods, services, and deployments
- Scales workloads and autoscales based on CPU metrics
- Manages secrets, config maps, and persistent volumes
K8s by the numbers
- 54 all-time installs (skills.sh)
- Ranked #705 of 1,039 Cloud & Infrastructure skills by installs in the Skillselion catalog
- Data as of Jul 28, 2026 (Skillselion catalog sync)
k8s capabilities & compatibility
- Works with
- kubernetes · aws · jenkins · docker
- Use cases
- devops · ci cd
- Pricing
- Free
What k8s says it does
This skill enables the AI to orchestrate and manage containerized applications using Kubernetes, focusing on scaling, deployment, and maintenance of pods, services, and deployments in a cluster.
Scale applications with deployments and replicasets, e.g., autoscaling based on CPU metrics.
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| Installs | 54 |
|---|---|
| repo stars | ★ 6 |
| Last updated | March 13, 2026 |
| Repository | alphaonedev/openclaw-graph ↗ |
What it does
Orchestrate containerized apps with Kubernetes - deploy, scale, and manage pods and services.
Who is it for?
Deploying, scaling, and managing containerized workloads in a Kubernetes cluster.
Skip if: Basic single-container Docker workflows.
When should I use this skill?
Deploying multi-container apps, scaling workloads, or troubleshooting a Kubernetes cluster.
What you get
Containerized workloads are deployed, scaled, and maintained in a Kubernetes cluster.
- Deployed pods and services
- Scaled deployments
- Configured secrets and config maps
By the numbers
- Documents 7 core capabilities from pod deployment to secrets management
Files
k8s
Purpose
This skill enables the AI to orchestrate and manage containerized applications using Kubernetes, focusing on scaling, deployment, and maintenance of pods, services, and deployments in a cluster.
When to Use
Use this skill for deploying multi-container apps, scaling workloads dynamically, managing resources in production environments, or troubleshooting cluster issues. Apply it when handling container orchestration beyond basic Docker, such as in CI/CD pipelines or microservices architectures.
Key Capabilities
- Deploy and manage pods using YAML manifests or imperative commands.
- Scale applications with deployments and replicasets, e.g., autoscaling based on CPU metrics.
- Expose services via ClusterIP, NodePort, or LoadBalancer types.
- Handle storage with PersistentVolumes and PersistentVolumeClaims.
- Monitor and debug resources using built-in tools like kubectl logs and events.
- Integrate with networking plugins for service discovery and load balancing.
- Manage secrets and config maps for secure configuration.
Usage Patterns
Always authenticate with a valid kubeconfig file, set via the $KUBECONFIG environment variable. For declarative setups, write YAML files and apply them; for imperative tasks, use kubectl directly. Pattern: Load context with kubectl config use-context my-context, then perform actions. Include error checks in scripts, e.g., verify command exit codes.
Example 1: Deploy a simple Nginx pod.
- Create a pod:
kubectl run nginx-pod --image=nginx --port=80 - Expose it:
kubectl expose pod nginx-pod --type=NodePort --port=80 - Verify:
kubectl get pods -l run=nginx-pod
Example 2: Scale a deployment.
- Apply a deployment YAML:
kubectl apply -f deployment.yaml - Where deployment.yaml contains:
apiVersion: apps/v1 kind: Deployment metadata: name: my-app spec: replicas: 3 selector: matchLabels: app: my-app template: metadata: labels: app: my-app spec: containers: - name: my-container image: my-image - Scale it:
kubectl scale deployment my-app --replicas=5 - Check status:
kubectl get deployments my-app
Common Commands/API
Use kubectl for CLI interactions; for API access, target the Kubernetes API server at endpoints like /api/v1/pods. Always specify namespaces with --namespace flag if needed.
- Get resources:
kubectl get pods --namespace=default -o wide(flags: -o for output format, --namespace for scope) - Create resources:
kubectl apply -f pod.yaml --record(flags: -f for file, --record for history) - Delete resources:
kubectl delete deployment my-app --cascade=foreground(flags: --cascade for dependent cleanup) - Update resources:
kubectl set image deployment/my-app my-container=my-image:new-tag - API endpoints: Use curl with authentication, e.g.,
curl -k -H "Authorization: Bearer $KUBE_TOKEN" https://api.example.com/api/v1/namespaces/default/pods - Config formats: YAML for manifests, e.g.,
apiVersion: v1 kind: Pod metadata: name: example spec: containers: - name: example image: nginx - Environment setup: Export $KUBECONFIG=/path/to/config for authentication.
Integration Notes
Integrate Kubernetes with other tools via the Kubernetes API or operators. For authentication, use $KUBECONFIG for kubeconfig files or $KUBE_API_KEY for API tokens. Pattern: In scripts, check if $KUBECONFIG is set; if not, prompt or error out. For CI/CD, use tools like Argo CD or Jenkins plugins; example: Helm charts for packaging, installed via helm install my-chart ./chart-dir. Ensure compatibility with cloud providers like AWS EKS by setting provider-specific configs in kubeconfig.
Error Handling
Always check kubectl exit codes; if non-zero, use kubectl describe <resource> for details. Common errors: "NotFound" for missing resources—handle by checking existence first with kubectl get; "Forbidden" for permissions—verify RBAC roles. In code, wrap commands in try-catch blocks, e.g., in Python: import subprocess; try: subprocess.run(['kubectl', 'get', 'pods'], check=True) except subprocess.CalledProcessError as e: print(f"Error: {e}"). For API calls, handle HTTP errors like 403 or 500 by retrying with exponential backoff. Log events with kubectl get events --namespace=default to diagnose issues.
Graph Relationships
- Related to cluster: devops-sre (e.g., links to other devops skills like CI/CD tools).
- Connected via tags: kubernetes (e.g., relates to container management skills), k8s (synonym for kubernetes), containers (links to Docker or orchestration skills).
- Potential edges: This skill depends on networking and storage skills; it provides outputs for monitoring skills like Prometheus.
Related skills
FAQ
What can this skill do in Kubernetes?
Deploy and manage pods, scale deployments, expose services, handle persistent storage, and manage secrets and config maps.
How does it authenticate to a cluster?
Via a kubeconfig file set through the KUBECONFIG environment variable, or a bearer token for the API.