New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Vertex Engine Inspector

by jeremylongshore in jeremylongshore/tons-of-skills-marketplace

Inspect and validate Vertex AI Agent Engine deployments including Code Execution Sandbox, Memory Bank, A2A protocol compliance, and security posture.

MITAuto-check passedDevOps & Cloud

Install Vertex Engine Inspector

skills CLI
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill vertex-engine-inspector -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace vertex-engine-inspector --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/.curated/vertex-engine-inspector .claude/skills/vertex-engine-inspector && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
vertex-engine-inspector
GitHub stars
2.8k
Token cost
~1.7k tokens
SKILL.md length
709 words
Files
10 (incl. scripts, references)
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

Inspect and validate Vertex AI Agent Engine deployments including Code Execution Sandbox, Memory Bank, A2A protocol compliance, and security posture.

  • Works in 10 steps: Connect to the Agent Engine deployment… → Parse the runtime configuration: model… → Validate Code Execution Sandbox… → …
  • Asked to inspect
  • SKILL.md covers Overview, Prerequisites, Instructions and Output, plus 3 more sections
  • Runs Python and Shell scripts from its folder; calls gcloud

What it does

Vertex Engine Inspector is an agent skill from jeremylongshore/tons-of-skills-marketplace. 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".

Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 11 other files, including scripts and reference files (for example `references/ARD.md`, `references/PRD.md` and `references/errors.md`). Compatibility notes: Designed for Claude Code

It sits in DevOps & Cloud, covering Deployment and Agent memory. It works with Vertex AI, Agent2Agent Protocol, Google Cloud and Python. The repository describes itself as: Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com. The licence is MIT.

When your agent uses it

  • Asked to inspect
  • Audit an Agent Engine deployment
  • With inspect agent engine
  • Validate agent engine deployment

Example prompts

  • “inspect agent engine”
  • “validate agent engine deployment”
  • “check agent engine config”
  • “/vertex-engine-inspector”

Requirements

  • Python 3
  • A Bash shell
  • Compatibility (from SKILL.md): Designed for Claude Code
  • Pre-approved tools (allowed-tools): Read, Grep, Glob, Bash(cmd:*)

Workflow steps

10 steps, taken from the first numbered list in SKILL.md.

  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/Flash), tools enabled, VPC settings, and scaling policies
  3. Validate Code Execution Sandbox settings: confirm state TTL is 7-14 days, sandbox type is SECURE_ISOLATED, and IAM permissions are scoped…
  4. Check Memory Bank configuration: verify enabled status, retention policy (min 100 memories), Firestore encryption, indexing enabled, and…
  5. Test A2A protocol compliance by probing /.well-known/agent-card, POST /v1/tasks:send, and GET /v1/tasks/ endpoints for correct responses
  6. Audit security posture: validate IAM least-privilege roles, VPC Service Controls perimeter, Model Armor activation, encryption at rest and…
  7. Query Cloud Monitoring for performance metrics: request count, error rate (target < 5%), latency percentiles (p50/p95/p99), token usage…
  8. Assess monitoring and observability: confirm Cloud Monitoring dashboards, alerting policies, structured logging, OpenTelemetry tracing…
  9. Calculate weighted scores across all categories and determine overall production readiness status
  10. Generate a prioritized list of recommendations with estimated score improvement per remediation

What it can do on your machine

Read from SKILL.md and the folder at commit cfae287. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Grep
    • Glob
    • Bash(cmd:*)

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Python and Shell), which the agent can run.

    Shell commands in SKILL.md call:

    • gcloud

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • cloud.google.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Designed for Claude Code

    From compatibility in the SKILL.md frontmatter.

Context cost

Vertex Engine Inspector loads about 1.7k tokens when it runs, and up to ~12k if it reads all its reference files. Until then it costs about 126 tokens; SKILL.md has 709 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~126
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~12k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from jeremylongshore/tons-of-skills-marketplace at commit cfae287, republished under its MIT licence (© jeremylongshore). 709 words, ~1,689 tokens.

Download SKILL.mdSave it as .claude/skills/vertex-engine-inspector/SKILL.md (or your agent's skills folder). This skill also uses 9 other files; get the full folder from GitHub.
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:*)
compatibility
Designed for Claude Code
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

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/Flash), tools enabled, VPC settings, and scaling policies
  3. 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
  4. Check Memory Bank configuration: verify enabled status, retention policy (min 100 memories), Firestore encryption, indexing enabled, and auto-cleanup active
  5. Test A2A protocol compliance by probing /.well-known/agent-card, POST /v1/tasks:send, and GET /v1/tasks/<task-id> endpoints for correct responses
  6. 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
  7. 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
  8. Assess monitoring and observability: confirm Cloud Monitoring dashboards, alerting policies, structured logging, OpenTelemetry tracing, and Cloud Error Reporting are configured
  9. Calculate weighted scores across all categories and determine overall production readiness status
  10. Generate a prioritized list of recommendations with estimated score improvement per remediation

See ${CLAUDE_SKILL_DIR}/references/inspection-workflow.md for the phased inspection process and ${CLAUDE_SKILL_DIR}/references/inspection-categories.md for detailed check criteria.

Output

  • Inspection report in YAML format with per-category scores and overall readiness percentage
  • Runtime configuration summary: model, tools, VPC, scaling settings
  • A2A protocol compliance matrix: pass/fail for AgentCard, Task API, Status API
  • Security posture score with breakdown: IAM, VPC-SC, Model Armor, encryption, secrets
  • Performance metrics dashboard: error rate, latency percentiles, token usage, daily cost estimate
  • Prioritized recommendations with estimated score improvement per item

See ${CLAUDE_SKILL_DIR}/references/example-inspection-report.md for a complete sample report.

Show full SKILL.md (276 more words)Show less

Error Handling

ErrorCauseSolution
Agent metadata not accessibleInsufficient IAM permissions or incorrect agent IDVerify roles/aiplatform.user granted; confirm agent ID with client.agent_engines.list() via Python SDK
A2A AgentCard endpoint 404Agent not configured for A2A protocol or endpoint path incorrectCheck agent configuration for A2A enablement; verify /.well-known/agent-card path
Cloud Monitoring metrics emptyMonitoring API not enabled or no recent trafficRun gcloud services enable monitoring.googleapis.com; generate test traffic first
VPC-SC perimeter blocking accessInspector running outside VPC Service Controls perimeterAdd inspector service account to access level; use VPC-SC bridge or access policy
Code Execution TTL out of rangeState TTL set below 1 day or above 14 daysAdjust 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.

Examples

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.

Resources

  • Vertex AI Agent Engine Documentation -- deployment and configuration
  • A2A Protocol Specification -- AgentCard, Task API, protocol compliance
  • Cloud Monitoring API -- metrics queries and dashboard configuration
  • VPC Service Controls -- perimeter setup and access policies
  • Model Armor -- prompt injection protection configuration

© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 9 other files (scripts, references) in skills/.curated/vertex-engine-inspector of jeremylongshore/tons-of-skills-marketplace.

  • SKILL.md
  • references/ARD.md
  • references/PRD.md
  • references/errors.md
  • references/example-inspection-report.md
  • references/examples.md
  • references/inspection-categories.md
  • references/inspection-workflow.md
  • scripts/check-security.py
  • scripts/inspect-agent.sh

Open the folder on GitHubat commit cfae287

Compare with similar skills

Vertex Engine Inspector next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Vertex Engine Inspector compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Vertex Engine Inspector this skilljeremylongshore/tons-of-skills-marketplace2.8k—~1.7kAutomated safety check: PassMIT
Google Agents CLI Scaffoldpifferologo/cloud-agents-cli1291 repos~2.9kAutomated safety check: NotesApache-2.0
Google Cloud Solution Guided Gke AI Migrationgoogle/skills21k—~8.3kAutomated safety check: PassApache-2.0
Google AdkMindrally/skills271—~2.5kAutomated safety check: PassApache-2.0
AWS Cdk Developmentzxkane/aws-skills3672 repos~2.5kAutomated safety check: PassMIT
Senior DevOps Toolkitmaslennikov-ig/claude-code-orchestrator-kit2606 repos~1.1kAutomated safety check: NotesCustom licence

Similar skills

  • Google Agents CLI Scaffold

    pifferologo/cloud-agents-cli

    This skill should be used when the user wants to "create an agent project", "start a new ADK project", "build me a new agent", "add CI/CD to my project", "add deployment", "enhance my project", or…

    129 GitHub starsUsed in 1 repo~2.9k tokens
    DevOps & CloudAuto-check: notes

  • Google Cloud Solution Guided Gke AI Migration

    google/skills

    Official

    Guides the migration of existing AI workloads (Cloud Run, Gemini API, Gemini Enterprise Agent Platform) to self-hosted GKE inference using gcloud and kubectl.

    21k GitHub stars~8.3k tokensUpdated yesterday
    DevOps & CloudAuto-check passed

  • Google Adk

    Mindrally/skills

    Best practices for building AI agents with Google's Agent Development Kit (ADK) in Python, covering agent design, tools, sessions, memory, artifacts, evaluation, and deployment.

    271 GitHub stars~2.5k tokensUpdated 2 days ago
    AI & LLM EngineeringAuto-check passed

  • AWS Cdk Development

    zxkane/aws-skills

    AWS Cloud Development Kit (CDK) expert for building cloud infrastructure with TypeScript/Python.

    367 GitHub starsUsed in 2 repos~2.5k tokens
    DevOps & CloudAuto-check passed

  • Senior DevOps Toolkit

    maslennikov-ig/claude-code-orchestrator-kit

    Comprehensive DevOps skill for CI/CD, infrastructure automation, containerization, and cloud platforms (AWS, GCP, Azure). Includes pipeline setup…

    260 GitHub starsUsed in 6 repos~1.1k tokens
    DevOps & CloudAuto-check: notes

  • Kiln Check Finetune Deprecation

    Kiln-AI/Kiln

    Check Kiln's fine-tunable model list for deprecated or unsupported base models.

    5.2k GitHub stars~1.9k tokensUpdated yesterday
    AI & LLM EngineeringAuto-check: notes

More from jeremylongshore/tons-of-skills-marketplace

All 3,342 skills in this repo

  • Performing Security Code Review

    jeremylongshore/tons-of-skills-marketplace

    Execute this skill enables AI assistant to conduct a security-focused code review using the security-agent plugin.

    2.8k GitHub starsUsed in 2 repos~1.3k tokens
    Auto-check: notes

  • Adapting Transfer Learning Models

    jeremylongshore/tons-of-skills-marketplace

    Build this skill automates the adaptation of pre-trained machine learning models using transfer learning techniques.

    2.8k GitHub stars~1.1k tokensUpdated today
    Auto-check passed

  • Agent Context Loader

    jeremylongshore/tons-of-skills-marketplace

    Execute proactive auto-loading: automatically detects and loads agents.md files.

    2.8k GitHub stars~1.1k tokensUpdated today
    Auto-check passed

  • Aggregating Performance Metrics

    jeremylongshore/tons-of-skills-marketplace

    Aggregate and centralize performance metrics from applications, systems, databases, caches, and services.

    2.8k GitHub stars~1.2k tokensUpdated today
    Auto-check passed

  • Analyzing Capacity Planning

    jeremylongshore/tons-of-skills-marketplace

    Execute this skill enables AI assistant to analyze capacity requirements and plan for future growth.

    2.8k GitHub stars~947 tokensUpdated today
    Auto-check passed

  • Analyzing Database Indexes

    jeremylongshore/tons-of-skills-marketplace

    Process use when you need to work with database indexing. An agent skill from jeremylongshore/tons-of-skills-marketplace.

    2.8k GitHub stars~2k tokensUpdated today
    Auto-check passed

Works with

Categories

Questions about Vertex Engine Inspector

What does Vertex Engine Inspector do?

Inspect and validate Vertex AI Agent Engine deployments including Code Execution Sandbox, Memory Bank, A2A protocol compliance, and security posture. Vertex Engine Inspector is an agent skill from jeremylongshore/tons-of-skills-marketplace. Inspect and validate Vertex AI Agent Engine deployments including Code Execution Sandbox, Memory Bank, A2A protocol compliance, and security posture.

When should I use Vertex Engine Inspector?

Vertex Engine Inspector fits situations like: asked to inspect; audit an Agent Engine deployment; with inspect agent engine; validate agent engine deployment.

How do I install Vertex Engine Inspector in Claude Code?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill vertex-engine-inspector -a claude-code`. Or copy the skill folder (skills/.curated/vertex-engine-inspector in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/vertex-engine-inspector in your project. Claude Code loads it when a task matches its description.

How do I install Vertex Engine Inspector in Codex?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill vertex-engine-inspector -a codex`. Or copy the skill folder (skills/.curated/vertex-engine-inspector in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/vertex-engine-inspector in your project. Codex loads it when a task matches its description.

Can I use Vertex Engine Inspector in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill vertex-engine-inspector -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/vertex-engine-inspector, .gemini/skills/vertex-engine-inspector, .github/skills/vertex-engine-inspector and .opencode/skills/vertex-engine-inspector in your project.

What does Vertex Engine Inspector need to run?

Going by SKILL.md and its folder, Vertex Engine Inspector needs Python and a shell for the scripts in its folder and the command-line tools its instructions call (gcloud). Our summary lists: Python 3; A Bash shell. Its frontmatter pre-approves these tools: Read, Grep, Glob, Bash(cmd:*). Compatibility (from SKILL.md): Designed for Claude Code.

Does Vertex Engine Inspector access the network?

SKILL.md names 1 domain. As links in the text: cloud.google.com. This is read from the text; nothing was executed.

Is Vertex Engine Inspector safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Vertex Engine Inspector use?

Vertex Engine Inspector is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Vertex Engine Inspector use?

About 1.7k tokens (SKILL.md is roughly 6.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 11k tokens, read only when the agent opens those files.

What are the alternatives to Vertex Engine Inspector?

Skills that share tags, products or a category with Vertex Engine Inspector: Google Agents CLI Scaffold (pifferologo/cloud-agents-cli, 129 stars), Google Cloud Solution Guided Gke AI Migration (google/skills, 21k stars), Google Adk (Mindrally/skills, 271 stars) and AWS Cdk Development (zxkane/aws-skills, 367 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Vertex Engine Inspector?

jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,827 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 10, 2026.

Source: jeremylongshore/tons-of-skills-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.