From jeremy-gcp-starter-examples
Generates production-ready GCP code examples from official ADK, Genkit, Vertex AI, and Gemini repos. Adapts to Python/TypeScript/Go with security, monitoring, IaC for AI agents and apps.
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skills --plugin jeremy-gcp-starter-examplesThis skill is limited to using the following tools:
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.
Recommends ADK architectures, tool contracts, scaffolds, and deployment plans for production agents on Google Cloud Vertex AI Agent Engine.
Builds Vertex AI Agent Engine scaffolds with Gemini models, RAG, function calling, multi-modal capabilities, evaluation, and GCP deployment configs.
Provisions secure Vertex AI ADK infrastructure with Terraform: Agent Engine runtime, code execution sandbox, Memory Bank, VPC Service Controls, IAM roles, and networking.
Share bugs, ideas, or general feedback.
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.
gcloud CLI authenticated with appropriate IAM roles (Vertex AI User, Cloud Run Developer)npm install -g firebase-tools)${CLAUDE_SKILL_DIR}/references/code-example-categories.mdSee ${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.
| Error | Cause | Solution |
|---|---|---|
| Invalid GCP project or API not enabled | Vertex AI API disabled or project ID misconfigured | Run gcloud services enable aiplatform.googleapis.com; verify project ID in gcloud config list |
| Permission denied on Vertex AI resources | Service account missing required IAM roles | Grant roles/aiplatform.user and roles/run.developer; check VPC-SC perimeter allows access |
| Model not available in region | Requested Gemini model not deployed in specified location | Use us-central1 or europe-west4 where Gemini models are available; check regional availability docs |
| Quota exceeded for API calls | Rate limit hit on Vertex AI prediction endpoint | Request quota increase via Cloud Console; implement exponential backoff with jitter |
| Dependency version conflict | Incompatible versions of AI SDK, Genkit, or provider packages | Pin versions in package.json or requirements.txt; use lockfile to ensure reproducibility |
See ${CLAUDE_SKILL_DIR}/references/errors.md for additional error scenarios.
Scenario 1: ADK Agent with Code Execution -- Create a production ADK agent using google/adk-samples patterns. Enable Code Execution Sandbox with 14-day state TTL, configure Memory Bank for persistent context, apply VPC Service Controls and IAM least-privilege. Deploy to Vertex AI Agent Engine.
Scenario 2: Genkit RAG Flow -- Implement a retrieval-augmented generation system using Firebase Genkit. Define a retriever with text-embedding-gecko embeddings, connect to a vector database, build a RAG flow with Zod-validated input/output schemas. Deploy to Cloud Run with auto-scaling (2-10 instances).
Scenario 3: Gemini Multimodal Analysis -- Analyze video content using the generative-ai repository patterns. Create a multimodal prompt combining video URIs with text questions using Gemini 2.5 Pro. Include safety filter configuration, token counting for cost estimation, and structured output parsing.
See ${CLAUDE_SKILL_DIR}/references/example-interactions.md for detailed interaction examples.