Google Skills

Interacts with Google Cloud services using the gcloud CLI safely and efficiently, with command validation, data reduction, safety guardrails, and workflows for discovery and investigation.

Optimize GKE costs by rightsizing workloads, configuring Spot VMs, selecting machine types, and using Committed Use Discounts.

Guides usage of the Gemini API on Agent Platform with the Google Gen AI SDK, covering SDK usage (Python, JS/TS, Go, Java, C#), capabilities like multimodal inputs, tools, media generation, caching, batch prediction, and Live API.

Routes to specialized GKE sub-skills for cluster management, networking, security, scaling, and more on Google Kubernetes Engine.

Configures GKE autoscaling with HPA, VPA, and Node Auto-Provisioning using golden path defaults for cost optimization.

Configures GKE storage including PVCs, PersistentVolumes, Filestore, and GCS FUSE with best practices for production workloads.

Hardens Google Kubernetes Engine (GKE) clusters with Workload Identity, Secret Manager, RBAC, Binary Authorization, Network Policies, and Pod Security Standards.

Plans, executes, and validates Google Kubernetes Engine (GKE) cluster upgrades and maintenance operations for both Standard and Autopilot clusters, producing upgrade plans, checklists, and runbooks with gcloud commands.

Protects stateful GKE workloads by configuring backup plans, restore workflows, and disaster recovery using Backup for GKE.

Runs batch processing and high-performance computing (HPC) workloads on Google Kubernetes Engine (GKE), including job queues, parallel processing, and MPI workloads.

Deploys and optimizes AI/ML inference workloads on GKE, using GPUs, TPUs, and model servers.

Manages AlloyDB for PostgreSQL clusters, instances, and backups using gcloud CLI commands.

Run machine learning and generative AI tasks directly in BigQuery SQL using built-in functions for forecasting, anomaly detection, key driver analysis, and text generation.

Plans, configures, and manages GKE networking including private clusters, VPC-native configurations, Gateway API, DNS, ingress/egress, Dataplane V2, and IP planning.

Manage datasets, tables, and jobs in BigQuery. Run SQL queries, manage BigQuery resources, and perform basic data ingestion and analysis.

Provision Bigtable instances, design performant schemas, and query data using gcloud, cbt, or client libraries.

Sets up the Firebase CLI, installs Firebase agent skills, and configures project context for mobile and web app development.

Provides GKE golden path configuration defaults, production readiness checklists, and cluster default patterns for designing and verifying GKE clusters.

Configures GKE workload reliability with PodDisruptionBudgets, health probes, topology spread constraints, and graceful shutdown patterns.

Deploy and manage Cloud Run services, jobs, and worker pools on Google Cloud.

Creates and manages Cloud SQL instances, databases, and users for MySQL, PostgreSQL, and SQL Server using gcloud CLI commands.

Plans and configures multi-tenancy on GKE, covering namespace isolation, RBAC planning, resource quotas, LimitRanges, network isolation, and cost allocation.

Manage custom Agent resources on the Gemini Enterprise Agent Platform via REST API, including creation, configuration, listing, updating, and deletion with support for files, skills, and tools.

Configures GKE observability with Cloud Logging, Cloud Monitoring, and managed Prometheus for monitoring, logging, and metrics collection.

Generates Python code using BigQuery DataFrames (BigFrames), the pandas/scikit-learn-style API over BigQuery, for dataframe and ML workflows.

Containerizes applications and deploys them to Google Kubernetes Engine (GKE) with Dockerfiles, manifests, and best practices.

Configures, optimizes, and troubleshoots GKE ComputeClasses for Spot VMs with on-demand fallback, GPU/TPU targeting, machine family selection, and zone colocation.

Creates GKE clusters with golden path Autopilot defaults, supporting Standard and GPU workloads. Guides through project, region, and networking inputs, then provisions and verifies cluster settings.

Deploy open models or custom weights from Model Garden to Agent Platform endpoints, check deployment status, verify serving endpoints, or clean up resources by undeploying models and deleting endpoints.

Fine-tune open models or Gemini models using Agent Platform infrastructure, from environment setup through data preparation, job configuration, monitoring, and deployment.

Authenticate, initialize, and use the stateful Gemini Interactions API for multi-turn conversations, streaming, structured output, and function calling on the Gemini Enterprise Agent Platform.

Provides guidance on enabling and optimizing GKE Cluster Autoscaler, including Node Auto Provisioning, troubleshooting scale-up/down issues, and best practices for capacity management.

Validate enterprise workloads against Google Cloud best practices using public client libraries and the REST API to manage evaluations, rules, scanned resources, and validation results.

Authenticates and connects to Google Cloud Agent Platform for inference with Gemini and third-party OpenMaaS models (Llama, DeepSeek, Qwen). Generates code for multiple SDKs, configures endpoints, and troubleshoots common errors.

Guides authentication and authorization to Google Cloud services, covering human users, service identities, Application Default Credentials (ADC), and best practices for secure access.

Evaluates Google Cloud workloads against the Well-Architected Framework security pillar, identifies security requirements, and provides actionable recommendations for IAM, network security, data protection, and operational security.

Fetches IAM recommendations and security insights from Google Cloud for a specified organization, folder, or project, using MCP tools, gcloud CLI, or direct API calls.

Measures and improves the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology, including dataset creation, metric selection, failure analysis, and iterative improvement.

Sets up Google's agents-cli toolkit and guides through the full agent development lifecycle: scaffold, build, evaluate, deploy, publish, and monitor on Gemini Enterprise Agent Platform.

Evaluates Google Cloud workloads against the Reliability pillar of the Well-Architected Framework, providing actionable recommendations for building, deploying, and managing reliable systems.

Manage machine learning models in the Agent Platform Model Registry: list, describe, upload, update, and delete models and their versions.

Search, manage, and scaffold skills in the Gemini Enterprise Agent Platform Skill Registry.

Guides a developer through first steps on Google Cloud, including account setup, authentication, project creation, and billing linkage.

Evaluates Google Cloud workloads against the Sustainability pillar of the Well-Architected Framework, providing actionable recommendations to minimize environmental impact.

Creates, lists, retrieves, versions, and deletes managed prompts in Google Cloud Agent Platform using the Python SDK.

Manages GenAI tuning jobs in Agent Platform by listing, inspecting, or canceling ongoing model tuning jobs.

Integrate client-side video and audio ads using the IMA SDK across web, Android, iOS, and TV platforms with VAST/VMAP support.

Configures dynamic threshold alerting policies for Google Cloud Vertex AI Agent Platform agents, monitoring latency, error rates, and quality metrics using Terraform and PromQL.

Manages Agent Platform serving endpoints: create, list, describe, update, delete, and troubleshoot permission, quota, and resource busy errors.

Evaluates Google Cloud workloads against the Cost Optimization pillar of the Well-Architected Framework, identifies cost requirements and constraints, and provides actionable recommendations for building, deploying, and managing workloads cost-efficiently.

Enables the Google Analytics Data API, authenticates via gcloud, and creates customized reports using the v1beta client library.

Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK.

Guides through client library installation and authentication setup for the Data Manager API, including ADC configuration and required scopes.

Installs and configures the official Google Ads MCP Server to connect AI assistants to Google Ads accounts for querying campaigns and retrieving reporting metrics using natural language.

Programmatically configure Google Analytics accounts, properties, data streams, custom dimensions, conversion events, and integrations using the Admin API.

Migrate applications from Google AI Studio to Google Cloud Agent Platform, enabling enterprise features like IAM, billing, and SLAs.

Investigates Google Cloud networking issues by analyzing logs, metrics, and diagnostics, including VPC Flow Logs, NAT, firewall, threat logs, latency, throughput, and Connectivity Tests.

Guides developers through Google Ads API quickstart: credential setup, choosing from 6 client libraries/REST, configuring environments, and running a 'retrieve campaigns' script. Troubleshoots common setup errors.

Deploys a secure, enterprise-grade Google Cloud landing zone foundation with organization policies, resource hierarchy, billing association, and centralized logging and monitoring.

Generates operations-focused guidance for Google Cloud workloads based on the Operational Excellence pillar of the Well-Architected Framework, including assessment questions and validation checklists.