Imported from jeremylongshore/tons-of-skills-marketplace (skills/.curated/validator-expert/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill validator-expert. Copyright stays with the author (MIT).

Validator Expert

Current State

!gcloud config get-value project 2>/dev/null || echo 'no active project' !gcloud auth list --filter=status:ACTIVE --format="value(account)" 2>/dev/null || echo 'not authenticated'

Overview

Validate production readiness of Vertex AI Agent Engine deployments by executing weighted checks across five categories: security (30 points), monitoring (20 points), performance (25 points), compliance (15 points), and best practices (10 points). This skill produces a 0-100% composite score with pass/fail per check and prioritized remediation recommendations.

Prerequisites

• gcloud CLI authenticated with roles/aiplatform.viewer, roles/iam.securityReviewer, and roles/monitoring.viewer
• Access to the target Google Cloud project and Vertex AI Agent Engine deployment
• Cloud Monitoring API and Cloud Logging API enabled in the project
• Knowledge of the deployment's expected SLOs (latency targets, error rate thresholds)
• Read-only access to IAM policies, VPC-SC configurations, and service account bindings

Instructions

1. Retrieve the deployment configuration using the Python SDK (vertexai.Client().agent_engines.get(name)) or REST API (GET https://{LOCATION}-aiplatform.googleapis.com/v1/projects/{PROJECT}/locations/{LOCATION}/reasoningEngines/{ID}) and parse model, scaling, and feature settings
2. Run the security validation suite (see security checklist):
   • Check if Agent Identity is enabled (recommended over service accounts for 2025+ deployments)
   • If using service accounts, verify IAM roles follow least-privilege (roles/aiplatform.expressUser, not roles/aiplatform.admin)
   • Confirm VPC Service Controls perimeter is active and correctly scoped
   • Check encryption at rest (CMEK or Google-managed) and in-transit (TLS 1.3)
   • Scan configuration files and environment variables for hardcoded secrets
   • Validate Model Armor is enabled with roles/modelarmor.user granted
   • Check Memory Bank IAM Conditions for multi-tenant agents
3. Run the monitoring validation suite:
   • Verify Cloud Monitoring dashboards exist with required panels (request count, error rate, latency)
   • Confirm alerting policies cover error rate spikes, latency SLO breaches, and cost thresholds
   • Check token usage tracking is enabled with per-model granularity
   • Validate structured logging with severity levels and correlation IDs
   • Confirm latency SLOs are defined with p95 and p99 targets
4. Run the performance validation suite:
   • Verify auto-scaling is configured with appropriate min/max instance counts
   • Check resource limits (CPU, memory) match expected workload profile
   • Confirm caching strategy is implemented for repeated prompts or embeddings
   • Validate Code Execution Sandbox TTL is set between 7-14 days
   • Check Memory Bank retention policy (min 100 memories, auto-cleanup enabled)
5. Run the compliance validation suite:
   • Confirm audit logging is enabled for all admin and data access operations
   • Verify data residency meets regional requirements
   • Check privacy policies and data retention schedules
   • Validate backup and disaster recovery configuration
6. Calculate weighted scores per category and compute the overall production readiness percentage
7. Generate a prioritized recommendation list sorted by score impact per remediation effort

Output

• Production readiness score: 0-100% with status (READY >= 85%, NEEDS WORK 70-84%, NOT READY < 70%)
• Per-category breakdown: security (x/30), monitoring (x/20), performance (x/25), compliance (x/15), best practices (x/10)
• Pass/fail table for each individual check with evidence notes
• Prioritized remediation plan: action items ranked by score improvement per effort
• Comparison to previous validation run (if available) showing score delta

Error Handling

Examples

Scenario 1: Pre-Launch Validation -- Validate a new ADK agent before production launch. Run all five validation categories. Target score: 85%+ overall, with security score at 28/30 minimum. Generate remediation plan for any failing checks.

Scenario 2: Post-Incident Security Audit -- After a permission escalation incident, re-validate security posture. Focus on IAM least-privilege, service account bindings, and VPC-SC perimeter integrity. Compare scores against the last passing validation.

Scenario 3: Quarterly Compliance Review -- Execute compliance and monitoring validation suites for SOC 2 audit preparation. Verify audit logging coverage, data residency compliance, and backup/DR configuration. Export results as evidence artifacts.

Resources

Validation checklists (read the relevant one during each validation step):

• Security checklist — IAM, VPC-SC, encryption, Model Armor (30% weight)
• Monitoring checklist — dashboards, alerts, SLOs, logging (20% weight)
• Performance & compliance checklist — auto-scaling, caching, audit logs, DR (40% weight)

Official Google Cloud documentation:

• Vertex AI Security Best Practices
• Cloud Monitoring Alerting
• VPC Service Controls
• Model Armor
• Cloud Audit Logs