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Found 52 Skills
Kubernetes deployment, management, and troubleshooting. Activate for k8s, kubectl, pods, deployments, services, ingress, namespaces, and container orchestration tasks.
Diagnoses and fixes Kubernetes issues with interactive remediation. Use when pods crash (CrashLoopBackOff, OOMKilled), services unreachable (502/503, empty endpoints), deployments stuck (ImagePullBackOff, pending). Also use when tempted to run kubectl fix commands directly without presenting options, or when user says "just fix it" for K8s issues.
Secure 1Password CLI patterns for reading secrets, discovering vaults/items, and piping credentials to other tools. Use when reading from 1Password, rotating secrets, or piping credentials to wrangler/kubectl/etc. Triggers on op CLI, 1Password, secret rotation, or credential piping tasks.
Kubernetes workload patterns, resource management, RBAC, probes, autoscaling, ConfigMap/Secret handling, and kubectl debugging for production-grade deployments.
Use for kubectl, CNPG, and kustomize/helm operations in this repo, including rollouts and cluster debugging.
Systematic Kubernetes troubleshooting and incident response. Use this skill whenever the user mentions Kubernetes, K8s, kubectl, pods, containers, or clusters. Triggers include diagnosing CrashLoopBackOff, ImagePullBackOff, OOMKilled, or Pending pods, responding to production incidents, troubleshooting node NotReady or DiskPressure, debugging service connectivity or networking, investigating PVC or storage failures, analyzing performance degradation, checking cluster health, troubleshooting Helm releases, and conducting post-incident reviews.
Answer natural language questions and perform analysis on GKE cluster and workload costs using BigQuery billing exports, cost allocation data, and live cluster monitoring metrics. Use when querying GKE costs across projects, namespaces, or workloads, analyzing billing reports in BigQuery (`bq`), checking cluster cost budgets (`gcloud billing`), or diagnosing cost drivers like pod requests vs. actual utilization (`kubectl top`). Don't use for applying cost optimization changes, creating rightsizing manifests (VPA/MPA), or selecting ComputeClasses (use gke-cost-optimization instead).
Guides the migration of existing AI workloads (Cloud Run, Gemini API, Gemini Enterprise Agent Platform) to self-hosted GKE inference using gcloud and kubectl. Use when the user has an existing AI inference workload (on Cloud Run, the Gemini API, Gemini Enterprise Agent Platform, or a custom VM) and wants to move it to self-hosted inference on GKE, or asks follow-up questions during such a migration (hardware sizing, model staging, manifest generation, validation, traffic cutover). DO NOT use for brand new GKE inference deployments with no existing workload to migrate (use gke-inference instead). DO NOT use if the user intends to automate the migration via the Gemini Cloud Assist MCP server.
Progressive delivery with Argo Rollouts and Flagger. Use when implementing canary deployments, blue-green deployments, or traffic shifting strategies.
Kubernetes networking management for services, ingresses, endpoints, and network policies. Use when configuring connectivity, load balancing, or network isolation.
Manage vCluster (virtual Kubernetes clusters) instances using vind. Use when creating, managing, or operating lightweight virtual clusters for development, testing, or multi-tenancy.
Deploy and manage Kubernetes workloads with progressive delivery. Use for deployments, rollouts, blue-green, canary releases, scaling, and release management.