Testing patterns for PHPUnit and Playwright E2E tests. Use when writing tests, debugging test failures, setting up test coverage, or implementing test patterns for ActivityPub features.
日本語の概要は準備中です。原文の説明を表示しています。
Create production-ready Kubernetes manifests for Deployments, Services, ConfigMaps, and Secrets following best practices and security standards. Use when generating Kubernetes YAML manifests, creating K8s resources, or implementing production-grade Kubernetes configurations.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Step-by-step guidance for creating production-ready Kubernetes manifests including Deployments, Services, ConfigMaps, Secrets, and PersistentVolumeClaims.
This skill provides comprehensive guidance for generating well-structured, secure, and production-ready Kubernetes manifests following cloud-native best practices and Kubernetes conventions.
Use this skill when you need to:
Understand the workload:
Questions to ask:
Follow this structure:
apiVersion: apps/v1
kind: Deployment
metadata:
name: <app-name>
namespace: <namespace>
labels:
app: <app-name>
version: <version>
spec:
replicas: 3
selector:
matchLabels:
app: <app-name>
template:
metadata:
labels:
app: <app-name>
version: <version>
spec:
containers:
- name: <container-name>
image: <image>:<tag>
ports:
- containerPort: <port>
name: http
resources:
requests:
memory: "256Mi"
cpu: "250m"
limits:
memory: "512Mi"
cpu: "500m"
livenessProbe:
httpGet:
path: /health
port: http
initialDelaySeconds: 30
periodSeconds: 10
readinessProbe:
httpGet:
path: /ready
port: http
initialDelaySeconds: 5
periodSeconds: 5
env:
- name: ENV_VAR
value: "value"
envFrom:
- configMapRef:
name: <app-name>-config
- secretRef:
name: <app-name>-secret
Best practices to apply:
:latest)Reference: See references/deployment-spec.md for detailed deployment options
Choose the appropriate Service type:
ClusterIP (internal only):
apiVersion: v1
kind: Service
metadata:
name: <app-name>
namespace: <namespace>
labels:
app: <app-name>
spec:
type: ClusterIP
selector:
app: <app-name>
ports:
- name: http
port: 80
targetPort: 8080
protocol: TCP
LoadBalancer (external access):
apiVersion: v1
kind: Service
metadata:
name: <app-name>
namespace: <namespace>
labels:
app: <app-name>
annotations:
service.beta.kubernetes.io/aws-load-balancer-type: nlb
spec:
type: LoadBalancer
selector:
app: <app-name>
ports:
- name: http
port: 80
targetPort: 8080
protocol: TCP
Reference: See references/service-spec.md for service types and networking
For application configuration:
apiVersion: v1
kind: ConfigMap
metadata:
name: <app-name>-config
namespace: <namespace>
data:
APP_MODE: production
LOG_LEVEL: info
DATABASE_HOST: db.example.com
# For config files
app.properties: |
server.port=8080
server.host=0.0.0.0
logging.level=INFO
Best practices:
Reference: See assets/configmap-template.yaml for examples
For sensitive data:
apiVersion: v1
kind: Secret
metadata:
name: <app-name>-secret
namespace: <namespace>
type: Opaque
stringData:
DATABASE_PASSWORD: "changeme"
API_KEY: "secret-api-key"
# For certificate files
tls.crt: |
-----BEGIN CERTIFICATE-----
...
-----END CERTIFICATE-----
tls.key: |
-----BEGIN PRIVATE KEY-----
...
-----END PRIVATE KEY-----
Security considerations:
kubernetes.io/tls for TLS secretsFor stateful applications:
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: <app-name>-data
namespace: <namespace>
spec:
accessModes:
- ReadWriteOnce
storageClassName: gp3
resources:
requests:
storage: 10Gi
Mount in Deployment:
spec:
template:
spec:
containers:
- name: app
volumeMounts:
- name: data
mountPath: /var/lib/app
volumes:
- name: data
persistentVolumeClaim:
claimName: <app-name>-data
Storage considerations:
Add security context to Deployment:
spec:
template:
spec:
securityContext:
runAsNonRoot: true
runAsUser: 1000
fsGroup: 1000
seccompProfile:
type: RuntimeDefault
containers:
- name: app
securityContext:
allowPrivilegeEscalation: false
readOnlyRootFilesystem: true
capabilities:
drop:
- ALL
Security checklist:
Standard labels (recommended):
metadata:
labels:
app.kubernetes.io/name: <app-name>
app.kubernetes.io/instance: <instance-name>
app.kubernetes.io/version: "1.0.0"
app.kubernetes.io/component: backend
app.kubernetes.io/part-of: <system-name>
app.kubernetes.io/managed-by: kubectl
Useful annotations:
metadata:
annotations:
description: "Application description"
contact: "team@example.com"
prometheus.io/scrape: "true"
prometheus.io/port: "9090"
prometheus.io/path: "/metrics"
File organization options:
Option 1: Single file with --- separator
# app-name.yaml
---
apiVersion: v1
kind: ConfigMap
...
---
apiVersion: v1
kind: Secret
...
---
apiVersion: apps/v1
kind: Deployment
...
---
apiVersion: v1
kind: Service
...
Option 2: Separate files
manifests/
├── configmap.yaml
├── secret.yaml
├── deployment.yaml
├── service.yaml
└── pvc.yaml
Option 3: Kustomize structure
base/
├── kustomization.yaml
├── deployment.yaml
├── service.yaml
└── configmap.yaml
overlays/
├── dev/
│ └── kustomization.yaml
└── prod/
└── kustomization.yaml
Validation steps:
# Dry-run validation
kubectl apply -f manifest.yaml --dry-run=client
# Server-side validation
kubectl apply -f manifest.yaml --dry-run=server
# Validate with kubeval
kubeval manifest.yaml
# Validate with kube-score
kube-score score manifest.yaml
# Check with kube-linter
kube-linter lint manifest.yaml
Testing checklist:
Use case: Standard web API or microservice
Components needed:
Reference: See assets/deployment-template.yaml
Use case: Database or persistent storage application
Components needed:
Use case: Scheduled tasks or batch processing
Components needed:
Use case: Application with sidecar containers
Components needed:
The following templates are available in the assets/ directory:
deployment-template.yaml - Standard deployment with best practicesservice-template.yaml - Service configurations (ClusterIP, LoadBalancer, NodePort)configmap-template.yaml - ConfigMap examples with different data typessecret-template.yaml - Secret examples (to be generated, not committed)pvc-template.yaml - PersistentVolumeClaim templatesreferences/deployment-spec.md - Detailed Deployment specificationreferences/service-spec.md - Service types and networking detailsPods not starting:
kubectl describe pod <pod-name>kubectl get nodeskubectl get events --sort-by='.lastTimestamp'Service not accessible:
kubectl get endpoints <service-name>kubectl run debug --rm -it --image=busybox -- shConfigMap/Secret not loading:
kubectl get configmap,secretAfter creating manifests:
helm-chart-scaffolding - For templating and packaginggitops-workflow - For automated deploymentsk8s-security-policies - For advanced security configurationsまだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Testing patterns for PHPUnit and Playwright E2E tests. Use when writing tests, debugging test failures, setting up test coverage, or implementing test patterns for ActivityPub features.
日本語の概要は準備中です。原文の説明を表示しています。
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
日本語の概要は準備中です。原文の説明を表示しています。
Add unsigned integer (uint) type support to PyTorch operators by updating AT_DISPATCH macros. Use when adding support for uint16, uint32, uint64 types to operators, kernels, or when user mentions enabling unsigned types, barebones unsigned types, or uint support.
日本語の概要は準備中です。原文の説明を表示しています。
This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.
日本語の概要は準備中です。原文の説明を表示しています。
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
日本語の概要は準備中です。原文の説明を表示しています。
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
日本語の概要は準備中です。原文の説明を表示しています。