gcp-storage

Name: gcp-storage
Author: alphaonedev

github.com/alphaonedevCloud & DevOps55 installs

About gcp-storage

gcp-storage is a skill for planning Google Cloud provisioning and operations, aimed at developers and teams using GCP. It provides architecture guidance, cost estimates, and pre-flight requirements, while Google Cloud Python client libraries perform the operations.

Key features

01GCP service selection guidance
02Cost estimation and optimization
03IAM and identity federation prerequisites
04Infrastructure-as-code approach guidance
05Vertex AI agent workflow guidance

Use cases

01Compare GCP services for a workload
02Estimate and optimize cloud costs
03Check IAM requirements before provisioning
04Plan multi-agent workflows with Vertex AI

MCP support

The skill identifies Vertex AI Extensions as MCP-compatible. It does not describe specific MCP tools or resources exposed.

Install

Copy the command and run it in a terminal.

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

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

Details

From the project README

gcp-storage

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

Comments

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Details

Author: alphaonedev
Repository: alphaonedev/openclaw-graph
Category: Cloud & DevOps
Installs: 55

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