mcpField
G
gcp-bigquery

gcp-bigquery

github.com/alphaonedevData & databases44 installs
Repository

About gcp-bigquery

gcp-bigquery is a skill for planning Google Cloud architecture and operations involving BigQuery. It provides guidance on service choices, costs, identity prerequisites, and execution through official Google Cloud Python client libraries.

Key features

  1. 01Google Cloud service selection guidance
  2. 02Cost estimation and optimization
  3. 03IAM and identity pre-flight guidance
  4. 04IaC approach guidance

Use cases

  1. 01Planning BigQuery-related Google Cloud operations
  2. 02Estimating costs for cloud workloads
  3. 03Reviewing identity requirements before deployment

MCP support

The README mentions Vertex AI Extensions as MCP-compatible, but does not describe MCP-specific functions of this skill.

Install

Copy the command and run it in a terminal.

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

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

Details

From the project README

gcp-bigquery

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