Use when deploying or managing Kubernetes workloads. Invoke to create deployment manifests, configure pod security policies, set up service accounts, define network isolation rules, debug pod crashes, analyze resource limits, inspect container logs, or right-size workloads. Use for Helm charts, RBAC policies, NetworkPolicies, storage configuration, performance optimization, GitOps pipelines, and multi-cluster management.
npx skills add https://github.com/Jeffallan/claude-skills --skill kubernetes-specialist
kubectl rollout status, kubectl get pods -w, and kubectl describe pod <name> to confirm health; roll back with kubectl rollout undo if neededLoad detailed guidance based on context:
| Topic | Reference | Load When |
|-------|-----------|-----------|
| Workloads | references/workloads.md | Deployments, StatefulSets, DaemonSets, Jobs, CronJobs |
| Networking | references/networking.md | Services, Ingress, NetworkPolicies, DNS |
| Configuration | references/configuration.md | ConfigMaps, Secrets, environment variables |
| Storage | references/storage.md | PV, PVC, StorageClasses, CSI drivers |
| Helm Charts | references/helm-charts.md | Chart structure, values, templates, hooks, testing, repositories |
| Troubleshooting | references/troubleshooting.md | kubectl debug, logs, events, common issues |
| Custom Operators | references/custom-operators.md | CRD, Operator SDK, controller-runtime, reconciliation |
| Service Mesh | references/service-mesh.md | Istio, Linkerd, traffic management, mTLS, canary |
| GitOps | references/gitops.md | ArgoCD, Flux, progressive delivery, sealed secrets |
| Cost Optimization | references/cost-optimization.md | VPA, HPA tuning, spot instances, quotas, right-sizing |
| Multi-Cluster | references/multi-cluster.md | Cluster API, federation, cross-cluster networking, DR |
apiVersion: apps/v1
kind: Deployment
metadata:
name: my-app
namespace: my-namespace
labels:
app: my-app
version: "1.2.3"
spec:
replicas: 3
selector:
matchLabels:
app: my-app
template:
metadata:
labels:
app: my-app
version: "1.2.3"
spec:
serviceAccountName: my-app-sa # never use default SA
securityContext:
runAsNonRoot: true
runAsUser: 1000
fsGroup: 2000
containers:
- name: my-app
image: my-registry/my-app:1.2.3 # never use latest
ports:
- containerPort: 8080
resources:
requests:
cpu: "100m"
memory: "128Mi"
limits:
cpu: "500m"
memory: "512Mi"
livenessProbe:
httpGet:
path: /healthz
port: 8080
initialDelaySeconds: 15
periodSeconds: 20
readinessProbe:
httpGet:
path: /ready
port: 8080
initialDelaySeconds: 5
periodSeconds: 10
securityContext:
allowPrivilegeEscalation: false
readOnlyRootFilesystem: true
capabilities:
drop: ["ALL"]
envFrom:
- secretRef:
name: my-app-secret # pull credentials from Secret, not ConfigMap
apiVersion: v1
kind: ServiceAccount
metadata:
name: my-app-sa
namespace: my-namespace
---
apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
name: my-app-role
namespace: my-namespace
rules:
- apiGroups: [""]
resources: ["configmaps"]
verbs: ["get", "list"] # grant only what is needed
---
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
name: my-app-rolebinding
namespace: my-namespace
subjects:
- kind: ServiceAccount
name: my-app-sa
namespace: my-namespace
roleRef:
kind: Role
name: my-app-role
apiGroup: rbac.authorization.k8s.io
# Deny all ingress and egress by default
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
name: default-deny-all
namespace: my-namespace
spec:
podSelector: {}
policyTypes: ["Ingress", "Egress"]
---
# Allow only specific traffic
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
name: allow-my-app
namespace: my-namespace
spec:
podSelector:
matchLabels:
app: my-app
policyTypes: ["Ingress"]
ingress:
- from:
- podSelector:
matchLabels:
app: frontend
ports:
- protocol: TCP
port: 8080
After deploying, verify health and security posture:
# Watch rollout complete
kubectl rollout status deployment/my-app -n my-namespace
# Stream pod events to catch crash loops or image pull errors
kubectl get pods -n my-namespace -w
# Inspect a specific pod for failures
kubectl describe pod <pod-name> -n my-namespace
# Check container logs
kubectl logs <pod-name> -n my-namespace --previous # use --previous for crashed containers
# Verify resource usage vs. limits
kubectl top pods -n my-namespace
# Audit RBAC permissions for a service account
kubectl auth can-i --list --as=system:serviceaccount:my-namespace:my-app-sa
# Roll back a failed deployment
kubectl rollout undo deployment/my-app -n my-namespace
When implementing Kubernetes resources, provide:
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take jeffallan/kubernetes-specialist from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.