agentsclimarketplace
Sign in

Vertex infra expert

Skill jeremylongshore/claude-code-plugins-plus-skills/plugins/devops/jeremy-vertex-terraform/skills/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.From its SKILL.md

Install
npx -y skills add jeremylongshore/claude-code-plugins-plus-skills --skill vertex-infra-expert

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

What its file declares

Copied from the file, not written here

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

2.8 KB, 430 tokens by cl100k_base, as published. Nobody here has run it

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

  • 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

What ships with it: 6 files

19.7 KB alongside SKILL.md, 1 of them executable

references/

scripts/

Keep looking

Skills are one crate of 326,144. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.