mcpField
G
gcp-compute

gcp-compute

github.com/alphaonedevCloud & DevOps52 installs
Repository

About gcp-compute

A skill that guides Google Cloud architecture and operations while leaving provisioning and execution to the official Google Cloud Python client libraries. It covers service selection, cost planning, identity prerequisites, infrastructure-as-code options, and Vertex AI agent patterns.

Key features

  1. 01Guidance on GCP service selection
  2. 02Cost estimation and optimization guidance
  3. 03IAM and Workload Identity Federation prerequisites
  4. 04Infrastructure-as-code approach guidance
  5. 05Vertex AI agent and multi-agent patterns

Use cases

  1. 01Compare GCP services for a workload
  2. 02Plan costs before provisioning
  3. 03Check identity requirements before deployment
  4. 04Design Vertex AI multi-agent workflows

MCP support

The skill identifies Vertex AI Extensions as MCP-compatible. It does not describe an MCP server interface.

Install

Copy the command and run it in a terminal.

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

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

Details

From the project README

gcp-compute

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

Comments

Markdown supported