Gcp Vertexai

Name: Gcp Vertexai
Author: alphaonedev

alphaonedev/openclaw-graph

50 installs
6 repo stars
Updated March 13, 2026
alphaonedev/openclaw-graph

gcp-vertexai is a Claude Code skill that guides building, deploying, and scaling machine learning models and LLM agents on Google Cloud Vertex AI.

About

gcp-vertexai is a skill that provides guidance for building, deploying, and scaling machine learning models and LLM agents on Google Cloud Vertex AI. It maps agent, model-serving, and RAG capabilities to Vertex AI tools like Agent Engine, Model Garden, and Vertex AI Search. A developer consults it when designing ML or multi-agent workflows on Vertex AI.

Guidance for building, deploying, and scaling ML models on Google Vertex AI
Covers Vertex AI Agent Engine, Model Garden, and RAG via Vertex AI Search
Delegates execution to the google-cloud-aiplatform SDK

Gcp Vertexai by the numbers

50 all-time installs (skills.sh)
Ranked #7,245 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
Data as of Jul 28, 2026 (Skillselion catalog sync)

At a glance

gcp-vertexai capabilities & compatibility

Requires a Google Cloud account and GCP billing; incurs Vertex AI usage cost

Capabilities: gcp gke · gcp storage · gcp terraform
Works with: gcp
Use cases: research · orchestration
Runs: Runs locally
Pricing: Bring your own API key

From the docs

What gcp-vertexai says it does

Platform for building, deploying, and scaling machine learning models on Google Cloud.

SKILL.md

Multi-agent | Agent Development Kit (google/adk-python)

SKILL.md

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

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Installs	50
repo stars	★ 6
Last updated	March 13, 2026
Repository	alphaonedev/openclaw-graph ↗

What it does

Consult it when building, deploying, or scaling ML models and LLM agents on Google Vertex AI.

Who is it for?

Vertex AI model serving, RAG, and multi-agent workflow design

When should I use this skill?

building or deploying ML models or LLM agents on Google Vertex AI

By the numbers

Maps 5 AI capabilities (LLM agents, model serving, RAG, multi-agent, MCP) to Vertex AI tools

Files

SKILL.mdMarkdownGitHub ↗

gcp-vertexai

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)