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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 claudepluginhub alexbramall/claude-code-skills --plugin engineering-skillsHow this skill is triggered — by the user, by Claude, or both
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/engineering-skills:gcp-cloud-architectThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Design scalable, cost-effective Google Cloud architectures for startups and enterprises with infrastructure-as-code templates.
Fetches up-to-date library and framework documentation via Context7 MCP for setup questions, API references, and code examples.
Share bugs, ideas, or general feedback.
Design scalable, cost-effective Google Cloud architectures for startups and enterprises with infrastructure-as-code templates.
Collect application specifications:
- Application type (web app, mobile backend, data pipeline, SaaS)
- Expected users and requests per second
- Budget constraints (monthly spend limit)
- Team size and GCP experience level
- Compliance requirements (GDPR, HIPAA, SOC 2)
- Availability requirements (SLA, RPO/RTO)
Run the architecture designer to get pattern recommendations:
python scripts/architecture_designer.py --input requirements.json
Example output:
{
"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"]
}
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:
python scripts/cost_optimizer.py --resources current_setup.json --monthly-spend 2000
Example output:
{
"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
}
Output includes:
Use the GCP Pricing Calculator for detailed estimates.
Create infrastructure-as-code for the selected pattern:
python scripts/deployment_manager.py --app-name my-app --pattern serverless_web --region us-central1
Example Terraform HCL output (Cloud Run + Firestore):
terraform {
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"
}
Example gcloud CLI deployment:
# Deploy Cloud Run service
gcloud run deploy my-app-api \
--image gcr.io/$PROJECT_ID/my-app:latest \
--region us-central1 \
--platform managed \
--allow-unauthenticated \
--memory 512Mi \
--cpu 1 \
--min-instances 0 \
--max-instances 10
# Create Firestore database
gcloud firestore databases create --location=us-central1
Full templates including Cloud CDN, Identity Platform, IAM, and Cloud Monitoring are generated by
deployment_manager.pyand also available inreferences/architecture_patterns.md.
Set up automated deployment with Cloud Build or GitHub Actions:
# cloudbuild.yaml
steps:
- name: 'gcr.io/cloud-builders/docker'
args: ['build', '-t', 'gcr.io/$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
Verify security configuration:
# 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=$POLICY_ID
Security checklist:
If deployment fails:
gcloud run services describe my-app-api --region us-central1
gcloud logging read "resource.type=cloud_run_revision" --limit=20
gcloud run deploy my-app-api --image gcr.io/$PROJECT_ID/my-app:latest --region us-central1
Common failure causes:
--allow-unauthenticated flaggcloud services enableRecommends GCP services based on workload requirements.
python scripts/architecture_designer.py --input requirements.json --output design.json
Input: JSON with app type, scale, budget, compliance needs Output: Recommended pattern, service stack, cost estimate, pros/cons
Analyzes GCP resources for cost savings.
python scripts/cost_optimizer.py --resources inventory.json --monthly-spend 5000
Output: Recommendations for:
Generates gcloud CLI deployment scripts and Terraform configurations.
python scripts/deployment_manager.py --app-name my-app --pattern serverless_web --region us-central1
Output: Production-ready deployment scripts with:
Ask: "Design a serverless web backend for a mobile app with 1000 users"
Result:
- Cloud Run for API (auto-scaling, no cold start with min instances)
- Firestore for data (pay-per-operation)
- Identity Platform for authentication
- Cloud Storage + Cloud CDN for static assets
- Estimated: $15-40/month
Ask: "Design a scalable architecture for a SaaS platform with 50k users"
Result:
- GKE Autopilot for containerized workloads
- Cloud SQL (PostgreSQL) with read replicas
- Memorystore (Redis) for session caching
- Cloud CDN for global delivery
- Cloud Build for CI/CD
- Multi-zone deployment
Ask: "Design a real-time analytics pipeline for event data"
Result:
- Pub/Sub for event ingestion
- Dataflow (Apache Beam) for stream processing
- BigQuery for analytics and warehousing
- Looker for dashboards
- Cloud Functions for lightweight transforms
Ask: "Design a machine learning platform for model training and serving"
Result:
- Vertex AI for training and prediction
- Cloud Storage for datasets and model artifacts
- BigQuery for feature store
- Cloud Functions for preprocessing triggers
- Cloud Monitoring for model drift detection
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:
{
"application_type": "saas_platform",
"expected_users": 10000,
"requests_per_second": 100,
"budget_monthly_usd": 500,
"team_size": 3,
"gcp_experience": "intermediate",
"compliance": ["SOC2"],
"availability_sla": "99.9%"
}
| 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 |