Prevent Kubernetes hallucinations by diagnosing and fixing failure modes: insecure workload defaults, resource starvation, network exposure, privilege sprawl, fragile rollouts, and API drift. Use when generating, reviewing, refactoring, or migrating manifests, Helm charts, Kustomize overlays, cluster policies, and platform-specific Kubernetes work for EKS, GKE, AKS, OpenShift, GitOps controllers, or observability stacks.
npx skills add https://github.com/LukasNiessen/kubernetes-skill --skill kubernetes-skill
Run this workflow top to bottom.
Record before writing manifests:
If unknown, state assumptions explicitly.
Select one or more based on user intent and risk:
Primary failure-mode references:
references/insecure-workload-defaults.mdreferences/resource-starvation.mdreferences/network-exposure.mdreferences/privilege-sprawl.mdreferences/fragile-rollouts.mdreferences/api-drift.mdSupplemental references (only when needed):
references/deployment-patterns.mdreferences/stateful-patterns.mdreferences/job-patterns.mdreferences/daemonset-operator-patterns.mdreferences/security-hardening.mdreferences/observability.mdreferences/multi-tenancy.mdreferences/storage-and-state.mdreferences/helm-patterns.mdreferences/kustomize-patterns.mdreferences/validation-and-policy.mdreferences/examples-good.mdreferences/examples-bad.mdreferences/do-dont-patterns.mdConditional Reference Retrieval (CRR) references (load only when the signal is detected):
references/conditional/eks-patterns.md for EKS, AWS, IRSA, EKS Pod Identity, AWS Load Balancer Controller, EBS/EFS CSI, Karpenterreferences/conditional/gke-patterns.md for GKE, Autopilot, Workload Identity Federation for GKE, Dataplane V2, GCE Ingress, Config Syncreferences/conditional/aks-patterns.md for AKS, Microsoft Entra Workload ID, Azure CNI, AGIC, Azure Disk/File/Blob CSIreferences/conditional/openshift-patterns.md for OpenShift, OKD, ROSA, ARO, Routes, SCCs, OLM, ocreferences/conditional/gitops-controllers.md for Argo CD, ApplicationSet, Flux, GitOps reconciliation, sync wavesreferences/conditional/observability-stacks.md for Prometheus Operator, ServiceMonitor, PodMonitor, OpenTelemetry, Loki, GrafanaDo not load multiple CRR files unless the task spans multiple detected platforms/tools.
For each fix, include:
When applicable, output:
Always provide validation steps tailored to deployment method and risk tier:
kubectl apply --dry-run=server or kubectl diffkubeconform for schema validation against target cluster versionNever recommend direct production apply without reviewed diff and approval.
Return:
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 lukasniessen/kubernetes-skill 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.