npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skills --plugin jeremy-vertex-engineThis skill is limited to using the following tools:
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.
Validates Vertex AI Agent Engine deployments for production readiness across security, monitoring, performance, compliance, and best practices. Generates weighted scores and remediation plans.
Builds, evaluates, and monitors AI agents using Opik: architecture patterns, metrics like hallucination and task completion, production observability, debugging, and best practices.
Builds Vertex AI Agent Engine scaffolds with Gemini models, RAG, function calling, multi-modal capabilities, evaluation, and GCP deployment configs.
Share bugs, ideas, or general feedback.
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.