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.

npx claudepluginhub flight505/skill-forge --plugin jeremy-vertex-terraform

Tool Access

This skill is limited to using the following tools:

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

Preview

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.

Supporting Assets

references/ARD.mdreferences/PRD.mdreferences/errors.mdreferences/examples.mdreferences/implementation.mdscripts/init-terraform.sh

SKILL.md

Similar Skills

cache-components

139.3k

Guides Next.js Cache Components and Partial Prerendering (PPR): 'use cache' directives, cacheLife(), cacheTag(), revalidateTag() for caching, invalidation, static/dynamic optimization. Auto-activates on cacheComponents: true.

cache-components

mcp-builder

124.2k

Guides building MCP servers enabling LLMs to interact with external services via tools. Covers best practices, TypeScript/Node (MCP SDK), Python (FastMCP).

9 files

anthropics-skills-13

Stats

Parent Repo Stars0

Parent Repo Forks0

Last CommitApr 30, 2026

Actions

View Source View Plugin View on GitHub View README

Help us improve

Share bugs, ideas, or general feedback.

Vertex Infra Expert

Overview

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

Configuration files or code changes applied to the project
Validation report confirming correct implementation
Summary of changes made and their rationale

See Terraform implementation details for output format specifications.

Error Handling

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

Examples

See ${CLAUDE_SKILL_DIR}/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 ${CLAUDE_SKILL_DIR}/vertex-examples/