Skills

Configures best-practice alerting policies for Google Cloud Vertex AI / Agent Platform agents on Agent Runtime. Use when analyzing, writing, or deploying alerting policies to monitor agent latency, error rates, and quality metrics (response quality, tool use, hallucination). Also use when provisioning online monitors for quality evaluation, or analyzing live metrics traffic footprints. NOTE: This skill currently only works for the Agent Runtime. Don't use for configuring general GCP alert policies or non-agent GCP alerting policies.

Connects to and performs inference with Google Cloud Agent Platform GenAI models, including First-Party Gemini models and Third-Party OpenMaaS models (Llama, DeepSeek, Qwen, etc.). Use when you need to generate code for calling Gemini or OpenMaaS models, authenticate with GenAI SDK, OpenAI SDK, or legacy Agent Platform SDK, configure base URLs and global/regional endpoints, or troubleshoot 429 Resource Exhausted (DSQ), 400 User Validation, or 404 Not Found errors. Don't use for deploying models to endpoints or for running model evaluations.

Build, review, refactor, or architect ASP.NET Core web applications using current official guidance for .NET web development. Use when working on Blazor Web Apps, Razor Pages, MVC, Minimal APIs, controller-based Web APIs, SignalR, gRPC, middleware, dependency injection, configuration, authentication, authorization, testing, performance, deployment, or ASP.NET Core upgrades.

Assists in provisioning instances/tables, designing performant schemas, and querying data in Bigtable. Use when designing Bigtable row keys, configuring column families, writing SQL queries or client library code (Java, Go, Python) for Bigtable, or diagnosing performance/hotspotting issues. Also use when provisioning Bigtable clusters using gcloud or cbt CLIs. Don't use for generic Cloud SQL administration.

Tests in real browsers via Chrome DevTools MCP. Use when building or debugging anything that runs in a browser. Use when you need to inspect the DOM, capture console errors, analyze network requests, profile performance, or verify visual output with real runtime data. Requires the chrome-devtools MCP server to be configured.

Automates CI/CD pipeline setup. Use when setting up or modifying build and deployment pipelines. Use when you need to automate quality gates, configure test runners in CI, or establish deployment strategies.

Optimizes agent context setup. Use when starting a new session, when agent output quality degrades, when switching between tasks, or when you need to configure rules files and context for a project.

Interacts with Google Cloud services using the gcloud CLI safely and efficiently. Covers command validation, data reduction, safety guardrails with a denylist, and workflows for discovery and investigation. You MUST read this skill before invoking any gcloud command. Use when managing cloud resources, querying configurations, or troubleshooting issues via gcloud. Don't use when writing or debugging Google Cloud client library code or raw REST/gRPC API interactions.

Manages custom Agent resources on Gemini Enterprise Agent Platform. Use when the user wants to programmatically create, configure, list, update, or delete stateful, server-managed Agent resources (including mounting files, skills, and tools) before executing conversations.

Clone/create/fork repos; manage remotes, releases.

Configures GKE Backup Plans and restore workflows. Use for backup policies, disaster recovery, or GKE cluster restores. Don't use for database backups.

Runs batch and HPC workloads on GKE, utilizing job queues and parallel processing. Use when running GKE batch jobs, configuring GKE HPC, or setting up GKE job queues. Don't use for standard web application deployments (use gke-app-onboarding instead).

Plans and executes GKE cluster creation, provisioning, and production readiness audits. Use when creating GKE clusters, provisioning GKE environments, or auditing GKE clusters. Don't use for application onboarding or deployment configuration (use gke-app-onboarding instead).

Configures, optimizes, and troubleshoots GKE ComputeClasses. Use when configuring Spot VMs with on-demand fallback, targeting specific accelerators (GPUs/TPUs) or machine families, restricting ComputeClass access, or debugging pending pods related to node pool auto-creation. Do not use for cluster-level Node Auto Provisioning configuration or general GKE cluster creation.

Optimizes GKE costs, rightsizes workloads, and configures Spot VMs and CUDs. Use when optimizing GKE costs, rightsizing GKE workloads, or configuring GKE Spot VMs. Don't use for general compute class provisioning or GPU Selection (use gke-compute-classes instead).

Provides GKE golden path configuration defaults, production readiness checklists, and cluster default patterns. Use when designing GKE clusters, verifying GKE production readiness, or checking configurations against GKE defaults. Don't use for setting up node autoscaling specifically (use gke-scaling instead).

Deploys and optimizes AI/ML inference workloads on GKE, using GPUs, TPUs, and model servers. Use when deploying GKE inference servers, configuring GKE GPU resources for inference, or deploying LLMs on GKE. Don't use for generic batch jobs or HPC task queues (use gke-batch-hpc instead).

Plans and configures multi-tenancy on GKE. Covers namespace isolation, RBAC planning for teams, resource quotas, LimitRanges, network isolation, and cost allocation. Use when designing GKE multi-tenancy, configuring GKE namespaces, setting up resource quotas, or isolating GKE teams. Don't use for single-tenant cluster configuration or general deployment instructions (use gke-basics or gke-app-onboarding instead).

Plans, configures, and manages GKE networking. Covers private clusters, VPC- native configurations, Gateway API, DNS, ingress/egress, Dataplane V2, and IP planning. Use when designing GKE networking layouts, configuring private clusters, setting up Gateway API, planning GKE IP ranges, or configuring GKE ingress/egress. Don't use for basic application routing that does not require dedicated network configuration.

Configures GKE observability, including Cloud Logging, Cloud Monitoring, and managed Prometheus. Use when configuring GKE monitoring, setting up GKE logging, or configuring Prometheus metrics collection. Don't use to configure local application logging frameworks or external APMs outside GKE.

Improves GKE workload reliability, using PDBs, health probes, and topology spread constraints. Use when configuring GKE workload reliability, setting up PDBs, or configuring GKE health probes (liveness, readiness, startup). Don't use for disaster recovery setup or full cluster backups (use gke-backup-dr instead).

Configures GKE autoscaling, including HPA, VPA, and Node Auto-Provisioning (NAP). Use when configuring GKE autoscaling, setting up GKE HPA, setting up GKE VPA, or configuring GKE NAP. Don't use for configuring static cluster sizes or setting node-level machine styles directly (use gke-compute-classes instead).

Plans, configures, and hardens Google Kubernetes Engine (GKE) security. Covers Workload Identity Federation, Secret Manager integration, RBAC hardening, Binary Authorization, Network Policies (Dataplane V2), Pod Security Standards, and IAM roles. Use when securing GKE clusters, setting up Workload Identity, hardening RBAC configurations, or configuring GKE secrets. Don't use for general network routing configuration (use gke-networking instead).

Manages GKE storage, including PVCs, PersistentVolumes, Filestore, and GCS FUSE. Use when configuring GKE storage, creating PVCs, or setting up GCS FUSE on GKE. Don't use for database administration or replication strategies outside volume provisioning context.