kubernetes-specialist▌
jeffallan/claude-skills · updated May 11, 2026
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Kubernetes workload deployment, configuration, security, and troubleshooting across single and multi-cluster environments.
- ›Covers Deployments, StatefulSets, DaemonSets, Jobs, Helm charts, RBAC policies, NetworkPolicies, and storage configuration with declarative YAML manifests
- ›Includes troubleshooting workflows for pod crashes, resource analysis, log inspection, and rollback procedures using kubectl commands
- ›Enforces security best practices: resource limits, health probes, least-priv
Kubernetes Specialist
When to Use This Skill
- Deploying workloads (Deployments, StatefulSets, DaemonSets, Jobs)
- Configuring networking (Services, Ingress, NetworkPolicies)
- Managing configuration (ConfigMaps, Secrets, environment variables)
- Setting up persistent storage (PV, PVC, StorageClasses)
- Creating Helm charts for application packaging
- Troubleshooting cluster and workload issues
- Implementing security best practices
Core Workflow
- Analyze requirements — Understand workload characteristics, scaling needs, security requirements
- Design architecture — Choose workload types, networking patterns, storage solutions
- Implement manifests — Create declarative YAML with proper resource limits, health checks
- Secure — Apply RBAC, NetworkPolicies, Pod Security Standards, least privilege
- Validate — Run
kubectl rollout status,kubectl get pods -w, andkubectl describe pod <name>to confirm health; roll back withkubectl rollout undoif needed
Reference Guide
Load 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 |
Constraints
MUST DO
- Use declarative YAML manifests (avoid imperative kubectl commands)
- Set resource requests and limits on all containers
- Include liveness and readiness probes
- Use secrets for sensitive data (never hardcode credentials)
- Apply least privilege RBAC permissions
- Implement NetworkPolicies for network segmentation
- Use namespaces for logical isolation
- Label resources consistently for organization
- Document configuration decisions in annotations
MUST NOT DO
- Deploy to production without resource limits
- Store secrets in ConfigMaps or as plain environment variables
- Use default ServiceAccount for application pods
- Allow unrestricted network access (default allow-all)
- Run containers as root without justification
- Skip health checks (liveness/readiness probes)
- Use latest tag for production images
- Expose unnecessary ports or services
Common YAML Patterns
Deployment with resource limits, probes, and security context
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
Minimal RBAC (least privilege)
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
NetworkPolicy (default-deny + explicit allow)
# 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
Validation Commands
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 how to use kubernetes-specialistHow to use kubernetes-specialist on Cursor
AI-first code editor with Composer
1Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add kubernetes-specialist
2Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/jeffallan/claude-skills --skill kubernetes-specialistThe skills CLI fetches kubernetes-specialist from GitHub repository jeffallan/claude-skills and configures it for Cursor.
3Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
◆ Which agents do you want to install to?││ ── Universal (.agents/skills) ── always included ────│ • Amp│ • Antigravity│ • Cline│ • Codex│ ●Cursor(selected)│ • Cursor│ • Windsurf4Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/kubernetes-specialistReload or restart Cursor to activate kubernetes-specialist. Access the skill through slash commands (e.g., /kubernetes-specialist) or your agent's skill management interface.
⚠Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
Additional Resources
List & Monetize Your Skill
Submit your Claude Code skill and start earning
GET_STARTED →Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
✓Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
✓Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
✓Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
general reviewsRatings
4.6★★★★★54 reviews- ★★★★★Charlotte Yang· Dec 20, 2024
Useful defaults in kubernetes-specialist — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Mia Menon· Dec 16, 2024
We added kubernetes-specialist from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Hana Flores· Dec 12, 2024
kubernetes-specialist has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Mia Mehta· Dec 12, 2024
kubernetes-specialist reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Dhruvi Jain· Dec 4, 2024
Solid pick for teams standardizing on skills: kubernetes-specialist is focused, and the summary matches what you get after install.
- ★★★★★Oshnikdeep· Nov 23, 2024
We added kubernetes-specialist from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Mia Verma· Nov 11, 2024
Keeps context tight: kubernetes-specialist is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Charlotte Kapoor· Nov 11, 2024
Registry listing for kubernetes-specialist matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Arjun Diallo· Nov 7, 2024
Solid pick for teams standardizing on skills: kubernetes-specialist is focused, and the summary matches what you get after install.
- ★★★★★Arjun Abebe· Nov 3, 2024
kubernetes-specialist fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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