gcp-examples-expert

name	gcp-examples-expert
description	Generate production-ready Google Cloud code examples from official repositories including ADK samples, Genkit templates, Vertex AI notebooks, and Gemini patterns. Use when asked to "show ADK example" or "provide GCP starter kit". Trigger with relevant phrases based on skill purpose.
allowed-tools	Read, Write, Edit, Grep, Glob, Bash(cmd:*)
version	2.25.0
author	Jeremy Longshore <[e-mail]>
license	MIT
effort	medium
argument-hint	[framework or use-case]
tags	["ai","gcp","gcp-examples"]
compatibility	Designed for Claude Code, also compatible with Codex and OpenClaw

GCP Examples Expert

Overview

Generate production-ready Google Cloud Platform code examples sourced from official repositories including ADK samples, Agent Starter Pack, Firebase Genkit, Vertex AI samples, Generative AI examples, and AgentSmithy. This skill maps user requirements to the appropriate GCP framework and delivers working code with security, monitoring, and deployment best practices baked in.

Prerequisites

• Google Cloud project with billing enabled and Vertex AI API activated
• gcloud CLI authenticated with appropriate IAM roles (Vertex AI User, Cloud Run Developer)
• Node.js 18+ for Genkit/TypeScript examples or Python 3.10+ for ADK/Vertex AI examples
• Firebase CLI for Genkit deployments (npm install -g firebase-tools)
• API keys or service account credentials configured via Secret Manager (never hardcoded)

Instructions

1. Identify the target framework by matching the request to one of six categories: ADK agents, Agent Starter Pack, Genkit flows, Vertex AI training, Generative AI multimodal, or AgentSmithy orchestration
2. Select the appropriate source repository and code pattern from ${CLAUDE_SKILL_DIR}/references/code-example-categories.md
3. Adapt the template to the specified programming language (TypeScript, Python, or Go)
4. Configure security settings: IAM least-privilege service accounts, VPC Service Controls, Model Armor for prompt injection protection
5. Add monitoring instrumentation: Cloud Monitoring dashboards, alerting policies, structured logging, OpenTelemetry tracing
6. Set auto-scaling parameters with appropriate min/max instance counts for the deployment target
7. Include cost optimization: select Gemini 2.5 Flash for simple tasks, Gemini 2.5 Pro for complex reasoning, batch predictions for bulk workloads
8. Generate deployment configuration for the target platform (Cloud Run, Firebase Functions, or Vertex AI Endpoints)
9. Provide Terraform or IaC templates for reproducible infrastructure provisioning
10. Cite the source repository and link to official documentation for each pattern used

See ${CLAUDE_SKILL_DIR}/references/workflow.md for the phased workflow and ${CLAUDE_SKILL_DIR}/references/best-practices-applied.md for the full best-practices checklist.

Output

• Complete, runnable code example with imports, configuration, and error handling
• Deployment configuration (Cloud Run service YAML, Firebase function config, or Terraform module)
• Environment variable template listing required secrets and API keys
• Monitoring setup: dashboard JSON, alerting policy definitions, log-based metrics