vertex-engine-inspector

# Vertex Engine Inspector ## Overview 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. ## Prerequisites - `google-cloud-aiplatform[agent_engines]>=1.120.0` Python SDK installed - `gcloud` CLI authenticated (for IAM and monitoring queries — **not** for Agent Engine CRUD) - IAM roles: `roles/aiplatform.user` and `roles/monitoring.viewer` granted on the target project - Access to the target Google Cloud project hosting the Agent Engine deployment - `curl` for A2A protocol endpoint testing (AgentCard, Task API, Status API) - Cloud Monitoring API enabled for performance metrics retrieval - Familiarity with Vertex AI Agent Engine concepts: Code Execution Sandbox, Memory Bank, Model Armor **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`. ## Instructions 1. Connect to the Agent Engine deployment by retrieving agent metadata via the Python SDK (`client.agent_engines.get(name=...)`) 2. Parse the runtime configuration: model selection (Gemini 2.5 Pro...