Agent skill

Operating Kubernetes

by ancoleman in ancoleman/ai-design-components

Operating production Kubernetes clusters effectively with resource management, advanced scheduling, networking, storage, security hardening, and autoscaling.

MITAuto-check passedDevOps & Cloud

Install Operating Kubernetes

skills CLI
$ npx skills add ancoleman/ai-design-components --skill operating-kubernetes -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ancoleman/ai-design-components operating-kubernetes --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ancoleman/ai-design-components.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/operating-kubernetes .claude/skills/operating-kubernetes && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
operating-kubernetes
GitHub stars
526
Token cost
~3.6k tokens
SKILL.md length
921 words
Files
25 (incl. scripts, references)
Skills in repo
75
Repo updated
First seen
Licence
MIT

At a glance

Operating production Kubernetes clusters effectively with resource management, advanced scheduling, networking, storage, security hardening, and autoscaling.

  • Deploying workloads to Kubernetes
  • SKILL.md covers Purpose, When to Use This Skill, Resource Management and Advanced Scheduling, plus 6 more sections
  • Runs Python scripts from its folder; calls kubectl
  • Configuring cluster resources

What it does

Operating Kubernetes is an agent skill from ancoleman/ai-design-components. Operating production Kubernetes clusters effectively with resource management, advanced scheduling, networking, storage, security hardening, and autoscaling. Use when deploying workloads to Kubernetes, configuring cluster resources, implementing security policies, or troubleshooting operational issues.

Its SKILL.md is about 3.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 28 other files, including scripts and reference files (for example `examples/manifests/hpa-cpu-memory.yaml`, `examples/manifests/keda-rabbitmq.yaml` and `examples/manifests/networkpolicy-allow-frontend.yaml`).

It sits in DevOps & Cloud, covering Container orchestration, Authorization and RBAC and Security review. It works with Kubernetes. The repository describes itself as: Comprehensive UI/UX and Backend component design skills for AI-assisted development with Claude. The licence is MIT.

When your agent uses it

  • Deploying workloads to Kubernetes
  • Configuring cluster resources
  • Implementing security policies
  • Troubleshooting operational issues

Example prompts

  • “/operating-kubernetes”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 76551b7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python, from the files we listed), which the agent can run.

    Shell commands in SKILL.md call:

    • kubectl

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use kubectl, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Operating Kubernetes loads about 3.6k tokens when it runs, and up to ~31k if it reads all its reference files. Until then it costs about 81 tokens; SKILL.md has 921 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~81
When it runs · the whole SKILL.md, loaded when a task matches
~3.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~31k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from ancoleman/ai-design-components at commit 76551b7, republished under its MIT licence (© ancoleman). 921 words, ~3,573 tokens.

Download SKILL.mdSave it as .claude/skills/operating-kubernetes/SKILL.md (or your agent's skills folder). This skill also uses 24 other files; get the full folder from GitHub.
name
operating-kubernetes
description
Operating production Kubernetes clusters effectively with resource management, advanced scheduling, networking, storage, security hardening, and autoscaling. Use when deploying workloads to Kubernetes, configuring cluster resources, implementing security policies, or troubleshooting operational issues.

Kubernetes Operations

Purpose

Operating Kubernetes clusters in production requires mastery of resource management, scheduling patterns, networking architecture, storage strategies, security hardening, and autoscaling. This skill provides operations-first frameworks for right-sizing workloads, implementing high-availability patterns, securing clusters with RBAC and Pod Security Standards, and systematically troubleshooting common failures.

Use this skill when deploying applications to Kubernetes, configuring cluster resources, implementing NetworkPolicies for zero-trust security, setting up autoscaling (HPA, VPA, KEDA), managing persistent storage, or diagnosing operational issues like CrashLoopBackOff or resource exhaustion.

When to Use This Skill

Common Triggers:

  • "Deploy my application to Kubernetes"
  • "Configure resource requests and limits"
  • "Set up autoscaling for my pods"
  • "Implement NetworkPolicies for security"
  • "My pod is stuck in Pending/CrashLoopBackOff"
  • "Configure RBAC with least privilege"
  • "Set up persistent storage for my database"
  • "Spread pods across availability zones"

Operations Covered:

  • Resource management (CPU/memory, QoS classes, quotas)
  • Advanced scheduling (affinity, taints, topology spread)
  • Networking (NetworkPolicies, Ingress, Gateway API)
  • Storage operations (StorageClasses, PVCs, CSI)
  • Security hardening (RBAC, Pod Security Standards, policies)
  • Autoscaling (HPA, VPA, KEDA, cluster autoscaler)
  • Troubleshooting (systematic debugging playbooks)

Resource Management

Quality of Service (QoS) Classes

Kubernetes assigns QoS classes based on resource requests and limits:

Guaranteed (Highest Priority):

  • Requests equal limits for CPU and memory
  • Never evicted unless exceeding limits
  • Use for critical production services
yaml
resources:
  requests:
    memory: "512Mi"
    cpu: "500m"
  limits:
    memory: "512Mi"  # Same as request
    cpu: "500m"

Burstable (Medium Priority):

  • Requests less than limits (or only requests set)
  • Can burst above requests
  • Evicted under node pressure
  • Use for web servers, most applications
yaml
resources:
  requests:
    memory: "256Mi"
    cpu: "250m"
  limits:
    memory: "512Mi"  # 2x request
    cpu: "500m"

BestEffort (Lowest Priority):

  • No requests or limits set
  • First to be evicted under pressure
  • Use only for development/testing
Decision Framework: Which QoS Class?
Workload TypeQoS ClassConfiguration
Critical API/DatabaseGuaranteedrequests == limits
Web servers, servicesBurstablelimits 1.5-2x requests
Batch jobsBurstableLow requests, high limits
Dev/test environmentsBestEffortNo limits
Resource Quotas and LimitRanges

Enforce multi-tenancy with ResourceQuotas (namespace limits) and LimitRanges (per-container defaults):

yaml
# ResourceQuota: Namespace-level limits
apiVersion: v1
kind: ResourceQuota
metadata:
  name: team-quota
  namespace: team-alpha
spec:
  hard:
    requests.cpu: "10"
    requests.memory: "20Gi"
    limits.cpu: "20"
    limits.memory: "40Gi"
    pods: "50"

For detailed resource management patterns including Vertical Pod Autoscaler (VPA), see references/resource-management.md.

Advanced Scheduling

Node Affinity

Control which nodes pods schedule on with required (hard) or preferred (soft) constraints:

yaml
affinity:
  nodeAffinity:
    requiredDuringSchedulingIgnoredDuringExecution:
      nodeSelectorTerms:
      - matchExpressions:
        - key: node.kubernetes.io/instance-type
          operator: In
          values:
          - g4dn.xlarge  # GPU instance
Taints and Tolerations

Reserve nodes for specific workloads (inverse of affinity):

bash
# Taint GPU nodes to prevent non-GPU workloads
kubectl taint nodes gpu-node-1 workload=gpu:NoSchedule
yaml
# Pod tolerates GPU taint
tolerations:
- key: "workload"
  operator: "Equal"
  value: "gpu"
  effect: "NoSchedule"
Topology Spread Constraints

Distribute pods evenly across failure domains (zones, nodes):

yaml
topologySpreadConstraints:
- maxSkew: 1  # Max difference in pod count
  topologyKey: topology.kubernetes.io/zone
  whenUnsatisfiable: DoNotSchedule
  labelSelector:
    matchLabels:
      app: critical-app

For advanced scheduling patterns including pod priority and preemption, see references/scheduling-patterns.md.

Networking

NetworkPolicies (Zero-Trust Security)

Implement default-deny security with NetworkPolicies:

yaml
# Default deny all traffic
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: default-deny-all
  namespace: production
spec:
  podSelector: {}
  policyTypes:
  - Ingress
  - Egress
yaml
# Allow specific ingress (frontend → backend)
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: backend-allow-frontend
spec:
  podSelector:
    matchLabels:
      app: backend
  ingress:
  - from:
    - podSelector:
        matchLabels:
          app: frontend
    ports:
    - protocol: TCP
      port: 8080
Ingress vs. Gateway API

Ingress (Legacy):

  • Widely supported, mature ecosystem
  • Limited expressiveness
  • Use for existing applications

Gateway API (Modern):

  • Role-oriented design (cluster ops vs. app devs)
  • More expressive (HTTPRoute, TCPRoute, TLSRoute)
  • Recommended for new applications (GA in Kubernetes 1.29+)
yaml
# Gateway API example
apiVersion: gateway.networking.k8s.io/v1
kind: HTTPRoute
metadata:
  name: app-routes
spec:
  parentRefs:
  - name: production-gateway
  rules:
  - matches:
    - path:
        type: PathPrefix
        value: /api
    backendRefs:
    - name: backend
      port: 8080

For detailed networking patterns including service mesh integration, see references/networking.md.

Storage

StorageClasses (Define Performance Tiers)

StorageClasses define storage tiers for different workload needs:

yaml
# AWS EBS SSD (high performance)
apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: fast-ssd
provisioner: ebs.csi.aws.com
parameters:
  type: gp3
  iopsPerGB: "50"
  encrypted: "true"
volumeBindingMode: WaitForFirstConsumer
allowVolumeExpansion: true
reclaimPolicy: Delete
Storage Decision Matrix
WorkloadPerformanceAccess ModeStorage Class
DatabaseHighReadWriteOnceSSD (gp3/io2)
Shared filesMediumReadWriteManyNFS/EFS
Logs (temp)LowReadWriteOnceStandard HDD
ML modelsHighReadOnlyManyObject storage (S3)

Access Modes:

  • ReadWriteOnce (RWO): Single node read-write (most common)
  • ReadOnlyMany (ROX): Multiple nodes read-only
  • ReadWriteMany (RWX): Multiple nodes read-write (requires network storage)

For detailed storage operations including volume snapshots and CSI drivers, see references/storage.md.

Security

RBAC (Role-Based Access Control)

Implement least-privilege access with RBAC:

yaml
# Role (namespace-scoped)
apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
  name: pod-reader
  namespace: production
rules:
- apiGroups: [""]
  resources: ["pods", "pods/log"]
  verbs: ["get", "list", "watch"]
---
# RoleBinding (assign role to user)
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  name: read-pods
  namespace: production
subjects:
- kind: User
  name: jane@example.com
  apiGroup: rbac.authorization.k8s.io
roleRef:
  kind: Role
  name: pod-reader
  apiGroup: rbac.authorization.k8s.io
Pod Security Standards

Enforce secure pod configurations at the namespace level:

yaml
# Namespace with Restricted PSS (most secure)
apiVersion: v1
kind: Namespace
metadata:
  name: production
  labels:
    pod-security.kubernetes.io/enforce: restricted
    pod-security.kubernetes.io/audit: restricted
    pod-security.kubernetes.io/warn: restricted

Pod Security Levels:

  • Restricted: Most secure, removes all privilege escalations (use for applications)
  • Baseline: Minimally restrictive, prevents known escalations
  • Privileged: Unrestricted (only for system workloads)

For detailed security patterns including policy enforcement (Kyverno/OPA) and secrets management, see references/security.md.

Show full SKILL.md (363 more words)Show less

Autoscaling

Horizontal Pod Autoscaler (HPA)

Scale pod replicas based on CPU, memory, or custom metrics:

yaml
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: web-app-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: web-app
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  behavior:
    scaleDown:
      stabilizationWindowSeconds: 300  # Wait 5min before scaling down
KEDA (Event-Driven Autoscaling)

Scale based on events beyond CPU/memory (queues, cron schedules, Prometheus metrics):

yaml
apiVersion: keda.sh/v1alpha1
kind: ScaledObject
metadata:
  name: rabbitmq-scaler
spec:
  scaleTargetRef:
    name: message-processor
  minReplicaCount: 0   # Scale to zero when queue empty
  maxReplicaCount: 30
  triggers:
  - type: rabbitmq
    metadata:
      queueName: tasks
      queueLength: "10"  # Scale up when >10 messages
Autoscaling Decision Matrix
ScenarioUse HPAUse VPAUse KEDAUse Cluster Autoscaler
Stateless web app with traffic spikes✅❌❌Maybe
Single-instance database❌✅❌Maybe
Queue processor (event-driven)❌❌✅Maybe
Pods pending (insufficient nodes)❌❌❌✅

For detailed autoscaling patterns including VPA and cluster autoscaler configuration, see references/autoscaling.md.

Troubleshooting

Common Pod Issues

Pod Stuck in Pending:

bash
kubectl describe pod <pod-name>

# Common causes:
# - Insufficient CPU/memory: Reduce requests or add nodes
# - Node selector mismatch: Fix nodeSelector or add labels
# - PVC not bound: Create PVC or fix name
# - Taint intolerance: Add toleration or remove taint

CrashLoopBackOff:

bash
kubectl logs <pod-name>
kubectl logs <pod-name> --previous  # Check previous crash

# Common causes:
# - Application crash: Fix code or configuration
# - Missing environment variables: Add to deployment
# - Liveness probe failing: Increase initialDelaySeconds
# - OOMKilled: Increase memory limit or fix leak

ImagePullBackOff:

bash
kubectl describe pod <pod-name>

# Common causes:
# - Image doesn't exist: Fix image name/tag
# - Authentication required: Create imagePullSecrets
# - Network issues: Check NetworkPolicies, firewall rules

Service Not Accessible:

bash
kubectl get endpoints <service-name>  # Should list pod IPs

# If endpoints empty:
# - Service selector doesn't match pod labels
# - Pods aren't ready (readiness probe failing)
# - Check NetworkPolicies blocking traffic

For systematic troubleshooting playbooks including networking and storage issues, see references/troubleshooting.md.

Reference Documentation

Deep Dives
  • references/resource-management.md - Resource requests/limits, QoS classes, ResourceQuotas, VPA
  • references/scheduling-patterns.md - Node affinity, taints/tolerations, topology spread, priority
  • references/networking.md - NetworkPolicies, Ingress, Gateway API, service mesh integration
  • references/storage.md - StorageClasses, PVCs, CSI drivers, volume snapshots
  • references/security.md - RBAC, Pod Security Standards, policy enforcement, secrets
  • references/autoscaling.md - HPA, VPA, KEDA, cluster autoscaler configuration
  • references/troubleshooting.md - Systematic debugging playbooks for common failures
Examples
  • examples/manifests/ - Copy-paste ready YAML manifests
  • examples/python/ - Automation scripts (audit, cost analysis, validation)
  • examples/go/ - Operator development examples
Tools
  • scripts/validate-resources.sh - Audit pods without resource limits
  • scripts/audit-networkpolicies.sh - Find namespaces without NetworkPolicies
  • scripts/cost-analysis.sh - Resource cost breakdown by namespace
  • building-ci-pipelines - Deploy to Kubernetes from CI/CD (kubectl apply, Helm, GitOps)
  • observability - Monitor clusters and workloads (Prometheus, Grafana, tracing)
  • secret-management - Secure secrets in Kubernetes (External Secrets, Sealed Secrets)
  • testing-strategies - Test manifests and deployments (Kubeval, Conftest, Kind)
  • infrastructure-as-code - Provision Kubernetes clusters (Terraform, Cluster API)
  • gitops-workflows - Declarative cluster management (Flux, ArgoCD)

Best Practices Summary

Resource Management:

  • Always set CPU/memory requests and limits
  • Use VPA for automated rightsizing
  • Implement resource quotas per namespace
  • Monitor actual usage vs. requests

Scheduling:

  • Use topology spread constraints for high availability
  • Apply taints for workload isolation (GPU, spot instances)
  • Set pod priority for critical workloads

Networking:

  • Implement NetworkPolicies with default-deny
  • Use Gateway API for new applications
  • Apply rate limiting at ingress layer

Storage:

  • Use CSI drivers (not legacy provisioners)
  • Define StorageClasses per performance tier
  • Enable volume snapshots for stateful apps

Security:

  • Enforce Pod Security Standards (Restricted for apps)
  • Implement RBAC with least privilege
  • Use policy engines for guardrails (Kyverno/OPA)
  • Scan images for vulnerabilities

Autoscaling:

  • Use HPA for stateless workloads
  • Use KEDA for event-driven workloads
  • Enable cluster autoscaler with limits
  • Set PodDisruptionBudgets to prevent over-disruption

© ancoleman, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 24 other files (scripts, references) in skills/operating-kubernetes of ancoleman/ai-design-components.

  • SKILL.md
  • examples/manifests/hpa-cpu-memory.yaml
  • examples/manifests/keda-rabbitmq.yaml
  • examples/manifests/networkpolicy-allow-frontend.yaml
  • examples/manifests/networkpolicy-default-deny.yaml
  • examples/manifests/pod-security-restricted.yaml
  • examples/manifests/qos-burstable.yaml
  • examples/manifests/qos-guaranteed.yaml
  • examples/manifests/rbac-least-privilege.yaml
  • examples/manifests/storageclass-ssd.yaml
  • examples/python/generate_cost_report.py
  • examples/python/list_pods_without_limits.py
  • examples/python/validate_rbac.py
  • outputs.yaml
  • references/autoscaling.md
  • references/networking.md
  • references/resource-management.md
  • … and 8 more

Open the folder on GitHubat commit 76551b7

Compare with similar skills

Operating Kubernetes next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Operating Kubernetes compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Operating Kubernetes this skillancoleman/ai-design-components526—~3.6kAutomated safety check: PassMIT
Performing Kubernetes Etcd Security Assessmentmukul975/Anthropic-Cybersecurity-Skills34k—~1.9kAutomated safety check: PassApache-2.0
Operate Kubernetes Toolchaincyberful/cyberful135—~898Automated safety check: PassAGPL-3.0
KubeSphere Multi-Tenant Managementkubesphere/kubesphere17k1 repos~3.1kAutomated safety check: PassCustom licence
Kubernetes SpecialistJeffallan/claude-skills12k1 repos~2.1kAutomated safety check: PassMIT
Documentationhome-operations/kopiur113—~2.5kAutomated safety check: PassAGPL-3.0

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Works with

Categories

Questions about Operating Kubernetes

What does Operating Kubernetes do?

Operating production Kubernetes clusters effectively with resource management, advanced scheduling, networking, storage, security hardening, and autoscaling. Operating Kubernetes is an agent skill from ancoleman/ai-design-components. Operating production Kubernetes clusters effectively with resource management, advanced scheduling, networking, storage, security hardening, and autoscaling.

When should I use Operating Kubernetes?

Operating Kubernetes fits situations like: deploying workloads to Kubernetes; configuring cluster resources; implementing security policies; troubleshooting operational issues.

How do I install Operating Kubernetes in Claude Code?

Run `npx skills add ancoleman/ai-design-components --skill operating-kubernetes -a claude-code`. Or copy the skill folder (skills/operating-kubernetes in ancoleman/ai-design-components) into .claude/skills/operating-kubernetes in your project. Claude Code loads it when a task matches its description.

How do I install Operating Kubernetes in Codex?

Run `npx skills add ancoleman/ai-design-components --skill operating-kubernetes -a codex`. Or copy the skill folder (skills/operating-kubernetes in ancoleman/ai-design-components) into .agents/skills/operating-kubernetes in your project. Codex loads it when a task matches its description.

Can I use Operating Kubernetes in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ancoleman/ai-design-components --skill operating-kubernetes -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/operating-kubernetes, .gemini/skills/operating-kubernetes, .github/skills/operating-kubernetes and .opencode/skills/operating-kubernetes in your project.

What does Operating Kubernetes need to run?

Going by SKILL.md and its folder, Operating Kubernetes needs Python for the scripts in its folder and the command-line tools its instructions call (kubectl). Our summary lists: Python 3.

Does Operating Kubernetes access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Operating Kubernetes safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Operating Kubernetes use?

Operating Kubernetes is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Operating Kubernetes use?

About 3.6k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 28k tokens, read only when the agent opens those files.

What are the alternatives to Operating Kubernetes?

Skills that share tags, products or a category with Operating Kubernetes: Performing Kubernetes Etcd Security Assessment (mukul975/Anthropic-Cybersecurity-Skills, 34k stars), Operate Kubernetes Toolchain (cyberful/cyberful, 135 stars), KubeSphere Multi-Tenant Management (kubesphere/kubesphere, 17k stars) and Kubernetes Specialist (Jeffallan/claude-skills, 12k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Operating Kubernetes?

ancoleman (a GitHub user) maintains it in ancoleman/ai-design-components, which has 526 GitHub stars. The repository holds 75 skills in this directory. The repository was last updated on December 11, 2025.

Source: ancoleman/ai-design-components on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.