Vertex agent builder

Skill jeremylongshore/claude-code-plugins-plus-skills/plugins/ai-ml/jeremy-vertex-ai/skills/vertex-agent-builder

425 plugins, 2,810 skills, 200 agents for Claude Code. Open-source marketplace at tonsofskills.com with the ccpi CLI package manager.

Install

npx -y skills add jeremylongshore/claude-code-plugins-plus-skills --skill vertex-agent-builder

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What its author says it does

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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".

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SKILL.md

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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

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