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Found 64 Skills
This skill should be used when users need to interact with Kubernetes clusters via kubectl CLI. It covers pod management, deployment operations, log viewing, debugging, resource monitoring, scaling, ConfigMaps, Secrets, Services, and all standard kubectl operations. Supports multiple clusters (production, staging, local k3s) with predefined aliases. Triggers on requests mentioning Kubernetes, k8s, pods, deployments, containers, or cluster operations.
kubectl-mcp-server CLI commands for tool discovery, direct invocation, and diagnostics. Use when exploring available tools, calling tools from command line, or checking server health.
Debug Kubernetes pods, nodes, and workloads using kubectl debug. Covers ephemeral containers, pod copying, node debugging, debug profiles, and interactive troubleshooting sessions. Use when user mentions kubectl debug, debugging pods, ephemeral containers, node debugging, or interactive troubleshooting in Kubernetes clusters.
Browser automation for Kubernetes dashboards and web UIs. Use when interacting with Kubernetes Dashboard, Grafana, ArgoCD UI, or other web interfaces. Requires MCP_BROWSER_ENABLED=true.
Kubernetes diagnostics for metrics, health checks, resource comparisons, and cluster analysis. Use when analyzing cluster health, comparing environments, or gathering diagnostic data.
Kubernetes cluster operations: kubectl commands, manifest generation, Helm charts, RBAC, debugging, and deployment strategies.
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).
Safety guardrails for destructive commands. Warns before rm -rf, DROP TABLE, force-push, git reset --hard, kubectl delete, and similar destructive operations. User can override each warning. Use when touching prod, debugging live systems, or working in a shared environment. Use when asked to "be careful", "safety mode", "prod mode", or "careful mode".
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.
Use for kubectl, CNPG, and kustomize/helm operations in this repo, including rollouts and cluster debugging.
Use when debugging services on a user's dedicated server via SSH. Use when needing to run a command on the server, inspect pods, check container logs, view k8s resources, or run kubectl commands. Use when "service exec" is insufficient and you need server-level access. Use when user says "check my server", "run X on my server", "debug pod", "kubectl", "SSH into server", "check k8s", or "inspect cluster".
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.