Kubernetes operations: debugging, security, RBAC, and infrastructure tooling.
npx skills add https://github.com/notque/vexjoy-agent --skill kubernetes
Kubernetes debugging, security hardening, and infrastructure tooling. Covers pod triage, RBAC, network policies, and cobaltcore hypervisor components.
| Signal | Reference | Size |
|--------|-----------|------|
| CrashLoopBackOff, OOMKilled, config error, health check, liveness probe, ImagePullBackOff, Pending, FailedScheduling | references/crash-diagnosis.md | ~140 lines |
| service resolution, DNS, CoreDNS, port-forward, NetworkPolicy ingress/egress | references/network-debugging.md | ~50 lines |
| CPU throttling, memory limit, OOMKill, ephemeral storage, DiskPressure, debug container | references/resource-debugging.md | ~100 lines |
| RBAC, Role, RoleBinding, ClusterRole, ServiceAccount, least-privilege | references/rbac-patterns.md | ~60 lines |
| PodSecurity, SecurityContext, runAsNonRoot, readOnlyRootFilesystem, restricted, baseline | references/pod-security.md | ~90 lines |
| NetworkPolicy, default-deny, allow-list, namespace isolation | references/network-policies.md | ~70 lines |
| cosign, Kyverno, OPA, admission controller, Sealed Secrets, External Secrets | references/supply-chain.md | ~120 lines |
| kvm-exporter, metrics, prometheus, libvirt, hypervisor, collector, scrape, steal time, NUMA, cgroups, cloud hypervisor | references/cobalt-kvm-exporter.md | ~800 lines |
| cobaltcore concurrency, goroutine, semaphore, TryLock | references/cobalt-concurrency-patterns.md | ~200 lines |
| cobaltcore testing, mock, moq, Kind cluster | references/cobalt-testing-patterns.md | ~200 lines |
| kubernetes debugging process, triage flow, diagnosis routing | references/kubernetes-debugging.md | ~50 lines |
| kubernetes security process, RBAC + pod security + network hardening | references/kubernetes-security.md | ~50 lines |
| cobaltcore overview, KVM exporter architecture, component identification | references/cobalt-core.md | ~50 lines |
Loading rule. Read the references whose signals match the task before responding.
Determine which Kubernetes domain the request targets:
| Domain | Load references | Action |
|--------|----------------|--------|
| Pod failure, CrashLoop, OOM | crash-diagnosis, resource-debugging | Triage flow |
| Network, DNS, service resolution | network-debugging, network-policies | Connectivity diagnosis |
| RBAC, permissions, roles | rbac-patterns | Access control |
| Pod hardening, container security | pod-security | Security posture |
| Image signing, secrets, admission | supply-chain | Supply chain |
| Cobaltcore / KVM exporter | kvm-exporter + cobalt refs | Component-specific |
Always specify -n <namespace> explicitly in every kubectl command.
Gate: Domain identified and relevant references loaded.
For debugging: follow the triage flow — describe, logs, events, exec. Use read-only commands to gather evidence before proposing changes.
For security: provide concrete YAML manifests and specific configurations. Answer with reference-backed specifics, not generic advice.
For cobaltcore: use component-specific reference knowledge for architecture, metrics, configuration, and deployment details.
Gate: Specific, reference-backed diagnosis or response provided.
For debugging: confirm the fix resolves the symptom.
For security: validate against the misconfiguration table in supply-chain.md.
For cobaltcore: verify against component test patterns.
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 notque/kubernetes 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.