Agent-Skills.md

Agent Skills: Vertex AI Agent Builder

Build and deploy production-ready generative AI agents using Vertex AI, Gemini models, and Google Cloud infrastructure with RAG, function calling, and multi-modal capabilities

UncategorizedID: jeremylongshore/claude-code-plugins-plus-skills/vertex-agent-builder

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jeremylongshore/claude-code-plugins-plus-skills
jeremylongshoreLicense: NOASSERTION
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pnpm dlx add-skill https://github.com/jeremylongshore/claude-code-plugins-plus-skills/tree/HEAD/plugins/ai-ml/jeremy-vertex-ai/skills/vertex-agent-builder

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plugins/ai-ml/jeremy-vertex-ai/skills/vertex-agent-builder/SKILL.md

Skill Metadata

Name
vertex-agent-builder
Description
|

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: https://cloud.google.com/vertex-ai/docs/agent-engine