gcp-iam

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