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