vertex-engine-inspector

Inspect and validate Vertex AI Agent Engine deployments including Code Execution Sandbox, Memory Bank, A2A protocol compliance, and security posture. Generates production readiness scores. Use when asked to inspect, validate, or audit an Agent Engine deployment. Trigger with "inspect agent engine", "validate agent engine deployment", "check agent engine config", "audit agent engine security", "agent engine readiness check", "vertex engine health", or "reasoning engine status".

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Repository: jeremylongshore/tons-of-skills-marketplace
Last source activity: August 19, 2026 at 12:13
Detected SKILL.md language: English
Stars: 2,677
Forks: 392

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langchain-deploy-integration

Deploy a LangChain 1.0 / LangGraph 1.0 app to Cloud Run, Vercel, or LangServe correctly — with timeouts sized for chain length, cold-start mitigation, SSE anti-buffering headers, and Secret Manager over .env. Use when prepping a first production deploy, debugging a stream that hangs behind a proxy, or diagnosing p99 latency spikes. Trigger with "langchain deploy", "langchain cloud run", "langchain vercel python", "langchain langserve", or "langchain docker".

2026-08-26

langchain-langgraph-agents

Build a correct LangGraph 1.0 ReAct agent with create_react_agent — typed tools, error propagation, recursion caps, and stop conditions that actually stop. Use when writing a first tool-calling agent, migrating from AgentExecutor or initialize_agent, or diagnosing an agent that loops on vague prompts. Trigger with "langgraph agent", "create_react_agent", "langgraph tool calling", "AgentExecutor migration", or "agent loop cost".

2026-08-26

langchain-langgraph-human-in-loop

Build LangGraph 1.0 human-in-the-loop approval flows with interrupt_before / interrupt_after and Command(resume=...) — JSON-serializable state, clean resume semantics, and UI wiring for approval decisions. Use when adding an approval gate before an expensive tool call, wiring a Slack/web UI for agent approvals, or debugging a graph that crashes on interrupt. Trigger with "langgraph human in loop", "langgraph interrupt_before", "langgraph approval flow", "Command resume", "langgraph HITL".

2026-08-26

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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

Connect to the Agent Engine deployment by retrieving agent metadata via the Python SDK (client.agent_engines.get(name=...))

Parse the runtime configuration: model selection (Gemini 2.5 Pro/Flash), tools enabled, VPC settings, and scaling policies

Validate Code Execution Sandbox settings: confirm state TTL is 7-14 days, sandbox type is SECURE_ISOLATED, and IAM permissions are scoped to required GCP services only

Check Memory Bank configuration: verify enabled status, retention policy (min 100 memories), Firestore encryption, indexing enabled, and auto-cleanup active

Test A2A protocol compliance by probing /.well-known/agent-card, POST /v1/tasks:send, and GET /v1/tasks/<task-id> endpoints for correct responses

Audit security posture: validate IAM least-privilege roles, VPC Service Controls perimeter, Model Armor activation, encryption at rest and in transit, and absence of hardcoded credentials

Query Cloud Monitoring for performance metrics: request count, error rate (target < 5%), latency percentiles (p50/p95/p99), token usage, and cost estimates over the last 24 hours

Vertex Engine Inspector

Overview

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

Instructions

Connect to the Agent Engine deployment by retrieving agent metadata via the Python SDK (client.agent_engines.get(name=...))

Parse the runtime configuration: model selection (Gemini 2.5 Pro/Flash), tools enabled, VPC settings, and scaling policies

Validate Code Execution Sandbox settings: confirm state TTL is 7-14 days, sandbox type is SECURE_ISOLATED, and IAM permissions are scoped to required GCP services only

Check Memory Bank configuration: verify enabled status, retention policy (min 100 memories), Firestore encryption, indexing enabled, and auto-cleanup active

Test A2A protocol compliance by probing /.well-known/agent-card, POST /v1/tasks:send, and GET /v1/tasks/<task-id> endpoints for correct responses

Audit security posture: validate IAM least-privilege roles, VPC Service Controls perimeter, Model Armor activation, encryption at rest and in transit, and absence of hardcoded credentials

Query Cloud Monitoring for performance metrics: request count, error rate (target < 5%), latency percentiles (p50/p95/p99), token usage, and cost estimates over the last 24 hours

name	vertex-engine-inspector
description	Inspect and validate Vertex AI Agent Engine deployments including Code Execution Sandbox, Memory Bank, A2A protocol compliance, and security posture. Generates production readiness scores. Use when asked to inspect, validate, or audit an Agent Engine deployment. Trigger with "inspect agent engine", "validate agent engine deployment", "check agent engine config", "audit agent engine security", "agent engine readiness check", "vertex engine health", or "reasoning engine status".
allowed-tools	Read, Grep, Glob, Bash(cmd:*)
version	2.31.0
author	Jeremy Longshore <jeremy@intentsolutions.io>
license	MIT
argument-hint	<project-id> <agent-engine-id> [location]
effort	high
tags	["ai","deployment","security","compliance"]
compatibility	Designed for Claude Code

name	vertex-engine-inspector
description	Inspect and validate Vertex AI Agent Engine deployments including Code Execution Sandbox, Memory Bank, A2A protocol compliance, and security posture. Generates production readiness scores. Use when asked to inspect, validate, or audit an Agent Engine deployment. Trigger with "inspect agent engine", "validate agent engine deployment", "check agent engine config", "audit agent engine security", "agent engine readiness check", "vertex engine health", or "reasoning engine status".
allowed-tools	Read, Grep, Glob, Bash(cmd:*)
version	2.31.0
author	Jeremy Longshore <jeremy@intentsolutions.io>
license	MIT
argument-hint	<project-id> <agent-engine-id> [location]
effort	high
tags	["ai","deployment","security","compliance"]
compatibility	Designed for Claude Code

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

vertex-engine-inspector

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