vertex-infra-expert | jeremy-vertex-terraform | ClaudePluginHub

Skill

Community

vertex-infra-expert

From jeremy-vertex-terraform

927

Description

Execute use when provisioning Vertex AI infrastructure with Terraform. Trigger with phrases like "vertex ai terraform", "deploy gemini terraform", "model garden infrastructure", "vertex ai endpoints terraform", or "vector search terraform". Provisions Model Garden models, Gemini endpoints, vector search indices, ML pipelines, and production AI services with encryption and auto-scaling.

AI Summary

Provisions Vertex AI infrastructure with Terraform, including Gemini endpoints, Model Garden deployments, vector search indices, and ML pipelines. Triggers on phrases like "vertex ai terraform" or "deploy gemini terraform" to generate production-ready configurations with encryption, auto-scaling, and IAM policies.

Install

1

Add the repository(one-time)

$

/plugin marketplace add jeremylongshore/claude-code-plugins-plus-skills

2

Install the plugin

$

/plugin install jeremy-vertex-terraform@claude-code-plugins-plus

Tool Access

This skill is limited to using the following tools:

ReadWriteEditGrepGlobBash(terraform:*)Bash(gcloud:*)

Supporting Assets

View in Repository

references/errors.md

references/examples.md

scripts/init-terraform.sh

Skill Content

Vertex Infra Expert

Overview

Provision Vertex AI infrastructure with Terraform (endpoints, deployed models, vector search indices, pipelines) with production guardrails: encryption, autoscaling, IAM least privilege, and operational validation steps. Use this skill to generate a minimal working Terraform baseline and iterate toward enterprise-ready deployments.

Prerequisites

Before using this skill, ensure:

Google Cloud project with Vertex AI API enabled
Terraform 1.0+ installed
gcloud CLI authenticated with appropriate permissions
Understanding of Vertex AI services and ML models
KMS keys created for encryption (if required)
GCS buckets for model artifacts and embeddings

Instructions

Define AI Services: Identify required Vertex AI components (endpoints, vector search, pipelines)
Configure Terraform: Set up backend and define project variables
Provision Endpoints: Deploy Gemini or custom model endpoints with auto-scaling
Set Up Vector Search: Create indices for embeddings with appropriate dimensions
Configure Encryption: Apply KMS encryption to endpoints and data
Implement Monitoring: Set up Cloud Monitoring for model performance
Apply IAM Policies: Grant least privilege access to AI services
Validate Deployment: Test endpoints and verify model availability

Output

Error Handling

See {baseDir}/references/errors.md for comprehensive error handling.

Examples

See {baseDir}/references/examples.md for detailed examples.

Resources

Vertex AI Terraform: https://registry.terraform.io/providers/hashicorp/google/latest/docs/resources/vertex_ai_endpoint
Vertex AI documentation: https://cloud.google.com/vertex-ai/docs
Model Garden: https://cloud.google.com/model-garden
Vector Search guide: https://cloud.google.com/vertex-ai/docs/vector-search
Terraform examples in {baseDir}/vertex-examples/

Links

GitHub Stats

113 forks

Updated 4 days ago

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