gcp-vertexai

Google Cloud Integration

This skill delegates all GCP provisioning and operations to the official Google Cloud Python client libraries.

Core GCP client library

pip install google-cloud-python

Vertex AI + Agent Engine (AI/ML workloads)

pip install google-cloud-aiplatform

Specific service clients (install only what you need)

pip install google-cloud-bigquery # BigQuery pip install google-cloud-storage # Cloud Storage pip install google-cloud-pubsub # Pub/Sub pip install google-cloud-run # Cloud Run

SDK Docs: https://github.com/googleapis/google-cloud-python Vertex AI SDK: https://cloud.google.com/vertex-ai/docs/python-sdk/use-vertex-ai-python-sdk

Use the Google Cloud Python SDK for all GCP provisioning and operational actions. This skill provides architecture guidance, cost modeling, and pre-flight requirements — the SDK handles execution.

Architecture Guidance

Consult this skill for:

• GCP service selection and trade-off analysis

• Cost estimation and optimization (committed use discounts, sustained use)

• Pre-flight IAM / Workload Identity Federation requirements

• IaC approach (Terraform AzureRM vs Deployment Manager vs Config Connector)

• Integration patterns with Google Workspace and other GCP services

• Vertex AI Agent Engine for multi-agent workflow design

Agent & AI Capabilities

Capability Tool

LLM agents Vertex AI Agent Engine

Model serving Vertex AI Model Garden

RAG Vertex AI Search + Embeddings API

Multi-agent Agent Development Kit (google/adk-python)

MCP Vertex AI Extensions (MCP-compatible)

Reference

• Google Cloud Python Client

• Vertex AI Python SDK

• Google ADK

• GCP Pricing Calculator

• IAM Best Practices

• Workload Identity Federation