Install
$ agentstack add skill-jonathan0823-opencode-config-kubernetes-patterns ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Kubernetes Patterns Skill
Overview
This skill provides comprehensive Kubernetes deployment patterns for production workloads including resource management, networking configuration, security hardening, auto-scaling, and multi-environment setups.
Quick Start
Basic Deployment
apiVersion: apps/v1
kind: Deployment
metadata:
name: myapp
spec:
replicas: 3
selector:
matchLabels:
app: myapp
template:
metadata:
labels:
app: myapp
spec:
containers:
- name: myapp
image: myregistry/myapp:latest
ports:
- containerPort: 8080
resources:
requests:
memory: "256Mi"
cpu: "250m"
limits:
memory: "512Mi"
cpu: "500m"
Service Exposure
apiVersion: v1
kind: Service
metadata:
name: myapp
spec:
selector:
app: myapp
ports:
- port: 80
targetPort: 8080
type: ClusterIP
Ingress Configuration
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
name: myapp
annotations:
nginx.ingress.kubernetes.io/ssl-redirect: "true"
spec:
ingressClassName: nginx
tls:
- hosts:
- myapp.example.com
secretName: myapp-tls
rules:
- host: myapp.example.com
http:
paths:
- path: /
pathType: Prefix
backend:
service:
name: myapp
port:
number: 80
Core Concepts
1. Resource Management
- Requests: Guaranteed resources for scheduling
- Limits: Maximum resources allowed
- Quality of Service: Guaranteed, Burstable, BestEffort
2. Pod Lifecycle
- Pending: Scheduling/initialization
- Running: All containers running
- Succeeded: Completed successfully
- Failed: Terminated with error
- Unknown: State cannot be determined
3. Service Types
- ClusterIP: Internal cluster access only
- NodePort: Exposes port on each node
- LoadBalancer: Cloud provider load balancer
- ExternalName: Maps to external DNS
Production Patterns
Health Checks
livenessProbe:
httpGet:
path: /health
port: 8080
initialDelaySeconds: 30
periodSeconds: 10
timeoutSeconds: 5
failureThreshold: 3
readinessProbe:
httpGet:
path: /ready
port: 8080
initialDelaySeconds: 5
periodSeconds: 5
Graceful Shutdown
lifecycle:
preStop:
exec:
command: ["/bin/sh", "-c", "sleep 15"]
t terminationGracePeriodSeconds: 60
Security Context
securityContext:
runAsNonRoot: true
runAsUser: 1000
runAsGroup: 1000
fsGroup: 1000
readOnlyRootFilesystem: true
allowPrivilegeEscalation: false
seccompProfile:
type: RuntimeDefault
capabilities:
drop:
- ALL
add:
- NET_BIND_SERVICE
Scaling Strategies
Horizontal Pod Autoscaler
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
name: myapp
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: myapp
minReplicas: 3
maxReplicas: 20
metrics:
- type: Resource
resource:
name: cpu
target:
type: Utilization
averageUtilization: 70
- type: Resource
resource:
name: memory
target:
type: Utilization
averageUtilization: 80
behavior:
scaleUp:
stabilizationWindowSeconds: 60
policies:
- type: Percent
value: 100
periodSeconds: 15
scaleDown:
stabilizationWindowSeconds: 300
policies:
- type: Percent
value: 10
periodSeconds: 60
Vertical Pod Autoscaler
apiVersion: autoscaling.k8s.io/v1
kind: VerticalPodAutoscaler
metadata:
name: myapp
spec:
targetRef:
apiVersion: apps/v1
kind: Deployment
name: myapp
updatePolicy:
updateMode: "Auto"
resourcePolicy:
containerPolicies:
- containerName: myapp
minAllowed:
cpu: 50m
memory: 100Mi
maxAllowed:
cpu: 1000m
memory: 1Gi
controlledResources: ["cpu", "memory"]
Detailed References
See comprehensive guides in references/:
- [Deployment Patterns](references/deployment-patterns.md) - Deployments, StatefulSets, DaemonSets, Jobs
- [Networking](references/networking.md) - Services, Ingress, Network Policies, Service Mesh
- [Security](references/security.md) - RBAC, Pod Security, Network Policies, Secrets
- [Storage](references/storage.md) - Volumes, Persistent Volumes, ConfigMaps, StatefulSets
When to Use This Skill
Use this skill when:
- Deploying containerized applications to Kubernetes
- Configuring auto-scaling and resource management
- Setting up ingress and load balancing
- Implementing security policies and RBAC
- Managing secrets and configurations
- Troubleshooting pod issues
- Planning cluster capacity
- Migrating from Docker Compose to Kubernetes
Related Skills
@docker-patterns- Containerization basics@ci-cd-pipelines- CI/CD with Kubernetes@security-best-practices- Container and cluster security@observability-monitoring- Monitoring Kubernetes workloads@microservices-patterns- Service mesh and distributed systems
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: Jonathan0823
- Source: Jonathan0823/opencode-config
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.