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Agent skill

Vertex Agent Builder

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

Build and deploy generative AI agents on Vertex AI: Gemini model selection, RAG with grounded retrieval, function calling, multimodal extraction, evaluation, and Agent Engine deployment with…

MITAuto-check passedAI & LLM Engineering

Install Vertex Agent Builder

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

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

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace vertex-agent-builder --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-agent-builder .claude/skills/vertex-agent-builder && 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-agent-builder
GitHub stars
2.8k
Token cost
~898 tokens
SKILL.md length
334 words
Files
6 (incl. references)
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

Build and deploy generative AI agents on Vertex AI: Gemini model selection, RAG with grounded retrieval, function calling, multimodal extraction, evaluation, and Agent Engine deployment with…

  • Works in 6 steps: Clarify the agent’s job (user intents,… → Choose model + region and define… → Implement retrieval (if needed):… → …
  • Operating Vertex AI agents on Google Cloud
  • SKILL.md covers Overview, Prerequisites, Instructions and Output, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Vertex Agent Builder is an agent skill from jeremylongshore/tons-of-skills-marketplace. Build and deploy generative AI agents on Vertex AI: Gemini model selection, RAG with grounded retrieval, function calling, multimodal extraction, evaluation, and Agent Engine deployment with operational guardrails (logs, alerts, cost controls). Use when designing, deploying, or operating Vertex AI agents on Google Cloud. Trigger with "build a Vertex agent", "deploy to Agent Engine", or "wire up RAG on Vertex AI".

Its SKILL.md is about 900 tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including reference files (for example `ARD.md`, `PRD.md` and `references/errors.md`). Compatibility notes: Designed for Claude Code

It sits in AI & LLM Engineering, covering Building AI agents, Structured output and tool calling and Budgeting and forecasting. It works with Vertex AI and Google Cloud. 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

  • Operating Vertex AI agents on Google Cloud
  • With build a Vertex agent
  • Deploy to Agent Engine
  • Wire up RAG on Vertex AI

Example prompts

  • “build a Vertex agent”
  • “deploy to Agent Engine”
  • “wire up RAG on Vertex AI”
  • “/vertex-agent-builder”

Requirements

  • Compatibility (from SKILL.md): Designed for Claude Code
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Grep, Bash(cmd:*)

Workflow steps

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

  1. Clarify the agent’s job (user intents, inputs/outputs, latency and cost constraints).
  2. Choose model + region and define tool/function interfaces (schemas, error contracts).
  3. Implement retrieval (if needed): chunking, embeddings, index, and a “citation-first” response format.
  4. Add evaluation: golden prompts, offline checks, and a minimal online smoke test.
  5. Deploy (optional): provide the exact deployment command/config and verify endpoints + permissions.
  6. Add ops: logs/metrics, alerting, quota/cost guardrails, and rollback steps.

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
    • Write
    • Edit
    • Grep
    • Bash(cmd:*)

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    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 Agent Builder loads about 898 tokens when it runs, and up to ~5.9k if it reads all its reference files. Until then it costs about 109 tokens; SKILL.md has 334 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~109
When it runs · the whole SKILL.md, loaded when a task matches
~898
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.9k

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from jeremylongshore/tons-of-skills-marketplace at commit cfae287, republished under its MIT licence (© jeremylongshore). 334 words, ~898 tokens.

Download SKILL.mdSave it as .claude/skills/vertex-agent-builder/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
vertex-agent-builder
description
Build and deploy generative AI agents on Vertex AI: Gemini model selection, RAG with grounded retrieval, function calling, multimodal extraction, evaluation, and Agent Engine deployment with operational guardrails (logs, alerts, cost controls). Use when designing, deploying, or operating Vertex AI agents on Google Cloud. Trigger with "build a Vertex agent", "deploy to Agent Engine", or "wire up RAG on Vertex AI".
allowed-tools
Read, Write, Edit, Grep, Bash(cmd:*)
compatibility
Designed for Claude Code
version
2.2.0
author
Jeremy Longshore <jeremy@intentsolutions.io>
license
MIT
tags
vertex-ai, deployment, gcp

Vertex AI Agent Builder

Build and deploy production-ready agents on Vertex AI with Gemini models, retrieval (RAG), function calling, and operational guardrails (validation, monitoring, cost controls).

Overview

  • Produces an agent scaffold aligned with Vertex AI Agent Engine deployment patterns.
  • Helps choose models/regions, design tool/function interfaces, and wire up retrieval.
  • Includes an evaluation + smoke-test checklist so deployments don’t regress.

Prerequisites

  • Google Cloud project with Vertex AI API enabled
  • Permissions to deploy/operate Agent Engine runtimes (or a local-only build target)
  • If using RAG: a document source (GCS/BigQuery/Firestore/etc) and an embeddings/index strategy
  • Secrets handled via env vars or Secret Manager (never committed)

Instructions

  1. Clarify the agent’s job (user intents, inputs/outputs, latency and cost constraints).
  2. Choose model + region and define tool/function interfaces (schemas, error contracts).
  3. Implement retrieval (if needed): chunking, embeddings, index, and a “citation-first” response format.
  4. Add evaluation: golden prompts, offline checks, and a minimal online smoke test.
  5. Deploy (optional): provide the exact deployment command/config and verify endpoints + permissions.
  6. Add ops: logs/metrics, alerting, quota/cost guardrails, and rollback steps.

Output

  • A Vertex AI agent scaffold (code/config) with clear extension points
  • A retrieval plan (when applicable) and a validation/evaluation checklist
  • Optional: deployment commands and post-deploy health checks

Error Handling

  • Quota/region issues: detect the failing service/quota and propose a scoped fix.
  • Auth failures: identify the principal and missing role; prefer least-privilege remediation.
  • Retrieval failures: validate indexing/embedding dimensions and add fallback behavior.
  • Tool/function errors: enforce structured error responses and add regression tests.

Examples

Example: RAG support agent

  • Request: “Deploy a support bot that answers from our docs with citations.”
  • Result: ingestion plan, retrieval wiring, evaluation prompts, and a smoke test that verifies citations.

Example: Multimodal intake agent

  • Request: “Build an agent that extracts structured fields from PDFs/images and routes tasks.”
  • Result: schema-first extraction prompts, tool interface contracts, and validation examples.

Resources

  • Implementation patterns (model selection, RAG wiring, deployment config): ${CLAUDE_SKILL_DIR}/references/implementation.md
  • Worked examples (RAG support agent, multimodal extraction): ${CLAUDE_SKILL_DIR}/references/examples.md
  • Error-handling and recovery patterns: ${CLAUDE_SKILL_DIR}/references/errors.md
  • Product / architecture context: ${CLAUDE_SKILL_DIR}/PRD.md, ${CLAUDE_SKILL_DIR}/ARD.md
  • Vertex AI docs: https://cloud.google.com/vertex-ai/docs
  • Agent Engine docs:

© 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 5 other files (references) in skills/.curated/vertex-agent-builder of jeremylongshore/tons-of-skills-marketplace.

  • SKILL.md
  • ARD.md
  • PRD.md
  • references/errors.md
  • references/examples.md
  • references/implementation.md

Open the folder on GitHubat commit cfae287

Compare with similar skills

Vertex Agent Builder 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 Agent Builder compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Vertex Agent Builder this skilljeremylongshore/tons-of-skills-marketplace2.8k—~898Automated safety check: PassMIT
LangchainOrchestra-Research/AI-Research-SKILLs13k2 repos~3.2kAutomated safety check: PassMIT
Building Agent Systemstelagod/code-abyss244—~691Automated safety check: PassMIT
Retail Product Search Agentgoogle/adk-recipes10k—~3kAutomated safety check: PassApache-2.0
Google Agents CLI Adk Codepifferologo/cloud-agents-cli1291 repos~768Automated safety check: PassApache-2.0
Sap AI Coresecondsky/sap-skills462—~3.3kAutomated safety check: PassGPL-3.0

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Works with

Categories

Questions about Vertex Agent Builder

What does Vertex Agent Builder do?

Build and deploy generative AI agents on Vertex AI: Gemini model selection, RAG with grounded retrieval, function calling, multimodal extraction, evaluation, and Agent Engine deployment with…. Vertex Agent Builder is an agent skill from jeremylongshore/tons-of-skills-marketplace. Build and deploy generative AI agents on Vertex AI: Gemini model selection, RAG with grounded retrieval, function calling, multimodal extraction, evaluation, and Agent Engine deployment with operational guardrails (logs, alerts, cost controls).

When should I use Vertex Agent Builder?

Vertex Agent Builder fits situations like: operating Vertex AI agents on Google Cloud; with build a Vertex agent; deploy to Agent Engine; wire up RAG on Vertex AI.

How do I install Vertex Agent Builder in Claude Code?

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

How do I install Vertex Agent Builder in Codex?

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

Can I use Vertex Agent Builder 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-agent-builder -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-agent-builder, .gemini/skills/vertex-agent-builder, .github/skills/vertex-agent-builder and .opencode/skills/vertex-agent-builder in your project.

What does Vertex Agent Builder need to run?

SKILL.md names no scripts, command-line tools or credentials: Vertex Agent Builder is instructions for the agent only. Its frontmatter pre-approves these tools: Read, Write, Edit, Grep, Bash(cmd:*). Compatibility (from SKILL.md): Designed for Claude Code.

Does Vertex Agent Builder 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 Agent Builder 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. Review the folder before installing.

What licence does Vertex Agent Builder use?

Vertex Agent Builder 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 Agent Builder use?

About 898 tokens (SKILL.md is roughly 3.6k 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 5k tokens, read only when the agent opens those files.

What are the alternatives to Vertex Agent Builder?

Skills that share tags, products or a category with Vertex Agent Builder: Langchain (Orchestra-Research/AI-Research-SKILLs, 13k stars), Building Agent Systems (telagod/code-abyss, 244 stars), Retail Product Search Agent (google/adk-recipes, 10k stars) and Google Agents CLI Adk Code (pifferologo/cloud-agents-cli, 129 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Vertex Agent Builder?

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.