Vertex Infra Expert
DevOps & Securityvertex-infra-expertVertex Infra Expert is an Agent Skill for devops & security published in the jeremylongshore/claude-code-plugins-plus-skills repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.
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.
How to use this skill
Bring this guide into your coding agent with a prompt tailored to the tool you use.
- Open your project in Codex.
- Copy and paste the prompt below.
- Review files and risks before approving any action.
Inspect the Agent Skill at https://github.com/jeremylongshore/claude-code-plugins-plus-skills/blob/HEAD/plugins/devops/jeremy-vertex-terraform/skills/vertex-infra-expert/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/vertex-infra-expert, but wait for my approval before writing files or running scripts.
Copying this prompt does not install or run the skill. Review third-party files before use.
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
- 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:
- Terraform examples in ${CLAUDE_SKILL_DIR}/vertex-examples/