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SKILL.md
npx skills add alirezarezvani/claude-skills --skill gcp-cloud-architect
Design GCP architectures for startups and enterprises. Use when asked to design Google Cloud infrastructure, deploy to GKE or Cloud Run, configure BigQuery pipelines, optimize GCP costs, or migrate to GCP. Covers Cloud Run, GKE, Cloud Functions, Cloud SQL, BigQuery, and cost optimization.
npx skills add alirezarezvani/claude-skills --skill gcp-cloud-architect
Design scalable, cost-effective Google Cloud architectures for startups and enterprises with infrastructure-as-code templates.
Collect application specifications:
Run the architecture designer to get pattern recommendations:
Example output:
Select from recommended patterns:
See references/architecture_patterns.md for detailed pattern specifications.
Validation checkpoint: Confirm the recommended pattern matches the team's operational maturity and compliance requirements before proceeding to Step 3.
Analyze estimated costs and optimization opportunities:
Example output:
Output includes:
Use the GCP Pricing Calculator for detailed estimates.
Create infrastructure-as-code for the selected pattern:
Example Terraform HCL output (Cloud Run + Firestore):
Example gcloud CLI deployment:
Full templates including Cloud CDN, Identity Platform, IAM, and Cloud Monitoring are generated by
deployment_manager.pyand also available inreferences/architecture_patterns.md.
Set up automated deployment with Cloud Build or GitHub Actions:
Verify security configuration:
Security checklist:
If deployment fails:
Common failure causes:
--allow-unauthenticated flaggcloud services enableRecommends GCP services based on workload requirements.
Input: JSON with app type, scale, budget, compliance needs Output: Recommended pattern, service stack, cost estimate, pros/cons
Analyzes GCP resources for cost savings.
Output: Recommendations for:
Generates gcloud CLI deployment scripts and Terraform configurations.
Output: Production-ready deployment scripts with:
Provide these details for architecture design:
| Requirement | Description | Example |
|---|---|---|
| Application type | What you're building | SaaS platform, mobile backend |
| Expected scale | Users, requests/sec | 10k users, 100 RPS |
| Budget | Monthly GCP limit | $500/month max |
| Team context | Size, GCP experience | 3 devs, intermediate |
| Compliance | Regulatory needs | HIPAA, GDPR, SOC 2 |
| Availability | Uptime requirements | 99.9% SLA, 1hr RPO |
JSON Format:
| Anti-Pattern | Why It Fails | Better Approach |
|---|---|---|
| Using default VPC for production | No isolation, shared firewall rules | Create custom VPC with private subnets |
| Over-provisioning GKE node pools | Wasted cost on idle capacity | Use GKE Autopilot or cluster autoscaler |
| Storing secrets in environment variables | Visible in Cloud Console, logs | Use Secret Manager with Workload Identity |
| Ignoring sustained use discounts | Missing 20-30% automatic savings | Right-size VMs for consistent baseline usage |
| Single-region deployment for SaaS | One region outage = full downtime | Multi-region with Cloud Load Balancing |
| BigQuery on-demand for heavy workloads | Unpredictable costs at scale | Use BigQuery slots (flat-rate) for consistent workloads |
| Running Cloud Functions for long tasks | 9-minute timeout, cold starts | Use Cloud Run for tasks > 60 seconds |
| Skill | Relationship |
|---|---|
engineering-team/aws-solution-architect | AWS equivalent — same 6-step workflow, different services |
engineering-team/azure-cloud-architect | Azure equivalent — completes the cloud trifecta |
engineering-team/senior-devops | Broader DevOps scope — pipelines, monitoring, containerization |
engineering/terraform-patterns | IaC implementation — use for Terraform modules targeting GCP |
engineering/ci-cd-pipeline-builder | Pipeline construction — automates Cloud Build and deployment |
| Document | Contents |
|---|---|
references/architecture_patterns.md | 6 patterns: serverless, GKE microservices, three-tier, data pipeline, ML platform, multi-region |
references/service_selection.md | Decision matrices for compute, database, storage, messaging |
references/best_practices.md | Naming, labels, IAM, networking, monitoring, disaster recovery |
{
"recommended_pattern": "serverless_web",
"service_stack": ["Cloud Storage", "Cloud CDN", "Cloud Run", "Firestore", "Identity Platform"],
"estimated_monthly_cost_usd": 30,
"pros": ["Low ops overhead", "Pay-per-use", "Auto-scaling", "No cold starts on Cloud Run min instances"],
"cons": ["Vendor lock-in", "Regional limitations", "Eventual consistency with Firestore"]
}python scripts/cost_optimizer.py --resources current_setup.json --monthly-spend 2000{
"current_monthly_usd": 2000,
"recommendations": [
{ "action": "Right-size Cloud SQL db-custom-4-16384 to db-custom-2-8192", "savings_usd": 380, "priority": "high" },
{ "action": "Purchase 1-yr committed use discount for GKE nodes", "savings_usd": 290, "priority": "high" },
{ "action": "Move Cloud Storage objects >90 days to Nearline", "savings_usd": 75, "priority": "medium" }
],
"total_potential_savings_usd": 745
}python scripts/deployment_manager.py --app-name my-app --pattern serverless_web --region us-central1python scripts/deployment_manager.py --app-name my-app --pattern serverless_web --region us-central1terraform {
required_providers {
google = {
source = "hashicorp/google"
version = "~> 5.0"
}
}
}
provider "google" {
project = var.project_id
region = var.region
}
variable "project_id" {
description = "GCP project ID"
type = string
}
variable "region" {
description = "GCP region"
type = string
default = "us-central1"
}
resource "google_cloud_run_v2_service" "api" {
name = "${var.environment}-${var.app_name}-api"
location = var.region
template {
containers {
image = "gcr.io/${var.project_id}/${var.app_name}:latest"
resources {
limits = {
cpu = "1000m"
memory = "512Mi"
}
}
env {
name = "FIRESTORE_PROJECT"
value = var.project_id
}
}
scaling {
min_instance_count = 0
max_instance_count = 10
}
}
}
resource "google_firestore_database" "default" {
project = var.project_id
name = "(default)"
location_id = var.region
type = "FIRESTORE_NATIVE"
}# Deploy Cloud Run service
gcloud run deploy my-app-api \
--image gcr.io/$PROJECT_ID/my-app:latest \
--region us-central1 \
--platform managed \
--allow-unauthenticated \
--memory 512Mi \
--cpu 1 \
--min-instances 0 \
--max-instances 10
# Create Firestore database
gcloud firestore databases create --location=us-central1# cloudbuild.yaml
steps:
- name: 'gcr.io/cloud-builders/docker'
args: ['build', '-t', 'gcr.io/$PROJECT_ID/my-app:$COMMIT_SHA', '.']
- name: 'gcr.io/cloud-builders/docker'
args: ['push', 'gcr.io/$PROJECT_ID/my-app:$COMMIT_SHA']
- name: 'gcr.io/google.com/cloudsdktool/cloud-sdk'
entrypoint: gcloud
args:
- 'run'
- 'deploy'
- 'my-app-api'
- '--image=gcr.io/$PROJECT_ID/my-app:$COMMIT_SHA'
- '--region=us-central1'
- '--platform=managed'
images:
- 'gcr.io/$PROJECT_ID/my-app:$COMMIT_SHA'# Connect repo and create trigger
gcloud builds triggers create github \
--repo-name=my-app \
--repo-owner=my-org \
--branch-pattern="^main$" \
--build-config=cloudbuild.yaml# Review IAM bindings
gcloud projects get-iam-policy $PROJECT_ID --format=json
# Check service account permissions
gcloud iam service-accounts list --project=$PROJECT_ID
# Verify VPC Service Controls (if applicable)
gcloud access-context-manager perimeters list --policy=$POLICY_ID