How do I trigger the validation process?

You can trigger the skill by asking Claude to 'validate deployment', 'check production readiness', or 'run security audit vertex ai' in your terminal.

What does this skill validate in Vertex AI?

It validates security (IAM, VPC-SC), monitoring (SLOs, dashboards), performance (scaling, caching), and compliance (audit logs, data residency) for the Vertex AI Agent Engine.

Does this skill require special permissions?

The skill requires read-only access to your project files via Read, Grep, Glob, and Bash tools to perform its analysis safely without making unauthorized changes.

What is the output of the validator-expert skill?

It produces a structured report featuring a weighted readiness score from 0-100% and a list of actionable recommendations to fix identified gaps.

Vertex AI Production Validator

Name: Vertex AI Production Validator
Author: jeremylongshore

byjeremylongshore

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883

Security & Testing

Validates production readiness for Vertex AI Agent Engine deployments across security, monitoring, performance, and compliance standards.

About

The Vertex AI Production Validator is an expert-level Claude Code skill designed to audit and verify Vertex AI Agent Engine deployments. It performs comprehensive checks across critical domains including IAM security, VPC service controls, Cloud Monitoring integration, and performance optimization. By analyzing the infrastructure and configuration, it generates weighted readiness scores (0-100%) and provides actionable recommendations to ensure your AI agents meet enterprise-grade standards before going live.

Key Features

Monitoring & Observability: Checks for Cloud Monitoring dashboards, alerting policies, and latency SLOs.
Security Auditing: Verifies IAM least privilege, VPC Service Controls, and Model Armor configurations.
Compliance & Governance: Ensures audit logging, data residency, and backup protocols are in place.
Actionable Reporting: Generates weighted readiness scores with specific remediation steps.
883 GitHub stars
Performance Optimization: Validates auto-scaling, resource limits, and caching strategies.

Use Cases

Pre-deployment production readiness check for Vertex AI Agent Engine projects.
Performance benchmarking and SLO definition for AI-driven enterprise applications.
Security and compliance audit of existing GCP AI infrastructure configurations.

About

Key Features

Monitoring & Observability: Checks for Cloud Monitoring dashboards, alerting policies, and latency SLOs.
Security Auditing: Verifies IAM least privilege, VPC Service Controls, and Model Armor configurations.
Compliance & Governance: Ensures audit logging, data residency, and backup protocols are in place.
Actionable Reporting: Generates weighted readiness scores with specific remediation steps.
883 GitHub stars
Performance Optimization: Validates auto-scaling, resource limits, and caching strategies.

Use Cases

Pre-deployment production readiness check for Vertex AI Agent Engine projects.
Performance benchmarking and SLO definition for AI-driven enterprise applications.
Security and compliance audit of existing GCP AI infrastructure configurations.