AgentSkillsCN

vertex-infra-expert

当需要使用Terraform配置Vertex AI基础设施时，使用此流程。可通过“Vertex AI Terraform”、“部署Gemini Terraform”、“Model Garden基础设施”、“Vertex AI端点Terraform”或“向量搜索Terraform”等短语触发。配置Model Garden模型、Gemini端点、向量搜索索引、ML管道以及带加密与自动伸缩的生产AI服务。

SKILL.md
--- frontmatter
name: vertex-infra-expert
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.
allowed-tools: Read, Write, Edit, Grep, Glob, Bash(terraform:*), Bash(gcloud:*)
version: 1.0.0
author: Jeremy Longshore <jeremy@intentsolutions.io>
license: MIT

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

  1. •Define AI Services: Identify required Vertex AI components (endpoints, vector search, pipelines)
  2. •Configure Terraform: Set up backend and define project variables
  3. •Provision Endpoints: Deploy Gemini or custom model endpoints with auto-scaling
  4. •Set Up Vector Search: Create indices for embeddings with appropriate dimensions
  5. •Configure Encryption: Apply KMS encryption to endpoints and data
  6. •Implement Monitoring: Set up Cloud Monitoring for model performance
  7. •Apply IAM Policies: Grant least privilege access to AI services
  8. •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