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From jeremy-vertex-engine
Inspects Vertex AI Agent Engine deployments for config, code sandbox, memory bank, A2A compliance, security posture, performance, and monitoring. Generates readiness scores.
How this skill is triggered — by the user, by Claude, or both
Slash command
/jeremy-vertex-engine:vertex-engine-inspectorThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Inspect and validate Vertex AI Agent Engine deployments across seven categories: runtime configuration, Code Execution Sandbox, Memory Bank, A2A protocol compliance, security posture, performance metrics, and monitoring observability. This skill generates weighted production-readiness scores (0-100%) with actionable recommendations for each deployment.
Inspect and validate Vertex AI Agent Engine deployments across seven categories: runtime configuration, Code Execution Sandbox, Memory Bank, A2A protocol compliance, security posture, performance metrics, and monitoring observability. This skill generates weighted production-readiness scores (0-100%) with actionable recommendations for each deployment.
google-cloud-aiplatform[agent_engines]>=1.120.0 Python SDK installedgcloud CLI authenticated (for IAM and monitoring queries — not for Agent Engine CRUD)roles/aiplatform.user and roles/monitoring.viewer granted on the target projectcurl for A2A protocol endpoint testing (AgentCard, Task API, Status API)Important: There is no gcloud CLI surface for Agent Engine (no gcloud ai agents, gcloud ai reasoning-engines, or gcloud alpha ai agent-engines commands exist). All Agent Engine operations use the Python SDK via vertexai.Client() or vertexai.preview.reasoning_engines.
client.agent_engines.get(name=...))SECURE_ISOLATED, and IAM permissions are scoped to required GCP services only/.well-known/agent-card, POST /v1/tasks:send, and GET /v1/tasks/<task-id> endpoints for correct responsesSee ${CLAUDE_SKILL_DIR}/references/inspection-workflow.md for the phased inspection process and ${CLAUDE_SKILL_DIR}/references/inspection-categories.md for detailed check criteria.
See ${CLAUDE_SKILL_DIR}/references/example-inspection-report.md for a complete sample report.
| Error | Cause | Solution |
|---|---|---|
| Agent metadata not accessible | Insufficient IAM permissions or incorrect agent ID | Verify roles/aiplatform.user granted; confirm agent ID with client.agent_engines.list() via Python SDK |
| A2A AgentCard endpoint 404 | Agent not configured for A2A protocol or endpoint path incorrect | Check agent configuration for A2A enablement; verify /.well-known/agent-card path |
| Cloud Monitoring metrics empty | Monitoring API not enabled or no recent traffic | Run gcloud services enable monitoring.googleapis.com; generate test traffic first |
| VPC-SC perimeter blocking access | Inspector running outside VPC Service Controls perimeter | Add inspector service account to access level; use VPC-SC bridge or access policy |
| Code Execution TTL out of range | State TTL set below 1 day or above 14 days | Adjust TTL to 7-14 days for production; values above 14 days are rejected by Agent Engine |
See ${CLAUDE_SKILL_DIR}/references/errors.md for additional error scenarios.
Scenario 1: Pre-Production Readiness Check -- Inspect a newly deployed ADK agent before production launch. Run all 28 checklist items across security, performance, monitoring, compliance, and reliability. Target: overall score above 85% before approving production traffic.
Scenario 2: Security Audit After IAM Change -- Re-inspect security posture after modifying service account roles. Validate that least-privilege is maintained (target: IAM score 95%+), VPC-SC perimeter is intact, and Model Armor remains active.
Scenario 3: Performance Degradation Investigation -- Inspect an agent showing elevated error rates. Query 24-hour performance metrics, identify latency spikes at p95/p99, check auto-scaling behavior, and correlate with token usage patterns to isolate the root cause.
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skills --plugin jeremy-vertex-engineValidates Vertex AI Agent Engine deployments for production readiness across security, monitoring, performance, compliance, and best practices. Generates weighted scores and remediation plans.
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