Agent skill · software engineering · jeremylongshore
gcp-examples-expert
Generate production-ready Google Cloud code examples from official repositories
Why this skill is useful
Adds multiple executable scripts and Terraform templates for deploying GCP services with best practices that aren't publicly documented.
What it needs
Requires firebase-tools, gcloud, node, python installed locally. Requires google-cloud account access. About 3k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
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 ${CLAUDESKILLDIR}/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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/gcp-examples-expert--39c3da