# Jeremylongshore Claude Code Plugins Plus Skills Vertex Infra Expert > Vertex Infra Expert - Skill: `tomevault-io/jeremylongshore-claude-code-plugins-plus-skills-vertex-infra` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/jeremylongshore-claude-code-plugins-plus-skills-vertex-infra` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/jeremylongshore-claude-code-plugins-plus-skills-vertex-infra/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: DevOps & Infra - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/jeremylongshore-claude-code-plugins-plus-skills-vertex-infra --- # 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](${CLAUDE_SKILL_DIR}/references/implementation.md) for output format specifications. ## Error Handling See `${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/ --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/jeremylongshore) — claim your Tome and manage your conversions.