| name | gcp-iam |
| description | Manages identity and access control for Google Cloud resources using IAM policies and roles. |
| metadata | {"upstream":{"cluster":"cloud-gcp","tags":["gcp","iam","access-control"],"authorization_required":false,"scope":"general","model_hint":"claude-sonnet","embedding_hint":"gcp iam access management policies roles permissions google cloud security"},"category":"development","source":{"repository":"https://github.com/alphaonedev/openclaw-graph","path":"skills/cloud-gcp/gcp-iam","license_path":"LICENSE","commit":"74b6037eb3320f51167ff1a72dd5f5e22f0f30e7"}} |
gcp-iam
Google Cloud Integration
This skill delegates all GCP provisioning and operations to the official Google Cloud Python client libraries.
pip install google-cloud-python
pip install google-cloud-aiplatform
pip install google-cloud-bigquery
pip install google-cloud-storage
pip install google-cloud-pubsub
pip install google-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
