Individual skill
vertex-infra-expert
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.
plugins/devops/jeremy-vertex-terraform/skills/vertex-infra-expert/SKILL.md
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- Last checked
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- Repository last updated
- Nov 12, 2026
- License
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- ai Repository topic
- automation Repository topic
- claude-code Repository topic
- devops Repository topic
- mcp Repository topic
- ai-agents Repository topic
- anthropic Repository topic
- claude-code-plugins Repository topic
- developer-tools Repository topic
- llm Repository topic
- devops Declared skill tag
- deployment Declared skill tag
- terraform Declared skill tag
- ml Declared skill tag
- devops Derived category
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SKILL.mdcore · 2,843 bytesreferences/ARD.mddoc · 4,909 bytesreferences/PRD.mddoc · 3,844 bytesreferences/errors.mddoc · 862 bytesreferences/examples.mddoc · 4,757 bytesreferences/implementation.mddoc · 4,863 bytesscripts/init-terraform.shruns · 939 bytes
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# 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)