mcpField
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gcp-iam

gcp-iam

github.com/alphaonedevCloud & DevOps431 installs
Repository

About gcp-iam

A skill that provides architecture guidance for Google Cloud provisioning and operations, while delegating execution to the official Google Cloud Python client libraries. It covers service selection, cost planning, IAM prerequisites, infrastructure approaches, and AI workloads.

Key features

  1. 01GCP service selection and trade-off guidance
  2. 02Cost estimation and optimization guidance
  3. 03IAM and Workload Identity Federation pre-flight checks
  4. 04Infrastructure-as-code approach guidance
  5. 05Vertex AI and multi-agent design references

Use cases

  1. 01Comparing GCP services for a project
  2. 02Estimating and optimizing cloud costs
  3. 03Checking IAM prerequisites before deployment
  4. 04Planning Vertex AI agent workflows

MCP support

The skill identifies Vertex AI Extensions as MCP-compatible. Execution of GCP provisioning and operations is delegated to the Google Cloud Python client libraries.

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/alphaonedev/openclaw-graph --skill gcp-iam

After installing, type/gcp-iamin Claude Code to confirm it loaded.

Details

From the project README

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

CapabilityTool
LLM agentsVertex AI Agent Engine
Model servingVertex AI Model Garden
RAGVertex AI Search + Embeddings API
Multi-agentAgent Development Kit (google/adk-python)
MCPVertex AI Extensions (MCP-compatible)

Reference

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