gcp-cloud-architect

Q: How do I install gcp-cloud-architect?

Run npx skills add https://github.com/alirezarezvani/claude-skills --skill gcp-cloud-architect in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/gcp-cloud-architect/ (personal) or .claude/skills/gcp-cloud-architect/ (project).

by alirezarezvaniDevOps & Cloud

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.

Installs: 718
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Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/alirezarezvani/claude-skills --skill gcp-cloud-architect

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads gcp-cloud-architect automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

GCP Cloud Architect

Design scalable, cost-effective Google Cloud architectures for startups and enterprises with infrastructure-as-code templates.

Workflow

Step 1: Gather Requirements

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)

Step 2: Design Architecture

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:

Serverless Web: Cloud Storage + Cloud CDN + Cloud Run + Firestore
Microservices on GKE: GKE Autopilot + Cloud SQL + Memorystore + Cloud Pub/Sub
Serverless Data Pipeline: Pub/Sub + Dataflow + BigQuery + Looker
ML Platform: Vertex AI + Cloud Storage + BigQuery + Cloud Functions

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.

Step 3: Estimate Cost

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:

Monthly cost breakdown by service
Right-sizing recommendations
Committed use discount opportunities
Sustained use discount analysis
Potential monthly savings

Use the GCP Pricing Calculator for detailed estimates.

Step 4: Generate IaC

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.py and also available in references/architecture_patterns.md.

Step 5: Configure CI/CD

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

Step 6: Security Review

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:

IAM roles follow least privilege (prefer predefined roles over basic roles)
Service accounts use Workload Identity for GKE
VPC Service Controls configured for sensitive APIs
Cloud KMS encryption keys for customer-managed encryption
Cloud Audit Logs enabled for all admin activity
Organization policies restrict public access
Secret Manager used for all credentials

If deployment fails:

Check the failure reason:

gcloud run services describe my-app-api --region us-central1
gcloud logging read "resource.type=cloud_run_revision" --limit=20

Review Cloud Logging for application errors.
Fix the configuration or container image.

Redeploy:

gcloud run deploy my-app-api --image gcr.io/$PROJECT_ID/my-app:latest --region us-central1

Common failure causes:

IAM permission errors -- verify service account roles and --allow-unauthenticated flag
Quota exceeded -- request quota increase via IAM & Admin > Quotas
Container startup failure -- check container logs and health check configuration
Region not enabled -- enable the required APIs with gcloud services enable

Tools

architecture_designer.py

Recommends 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

cost_optimizer.py

Analyzes GCP resources for cost savings.

python scripts/cost_optimizer.py --resources inventory.json --monthly-spend 5000

Output: Recommendations for:

Idle resource removal
Machine type right-sizing
Committed use discounts
Storage class transitions
Network egress optimization

deployment_manager.py

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:

Cloud Run or GKE deployment
Firestore or Cloud SQL setup
Identity Platform configuration
IAM roles with least privilege
Cloud Monitoring and Logging

Quick Start

Web App on Cloud Run (< $100/month)

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

Microservices on GKE ($500-2000/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

Serverless Data Pipeline

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

ML Platform

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

Input Requirements

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%"
}

Output Formats

Architecture Design

Pattern recommendation with rationale
Service stack diagram (ASCII)
Monthly cost estimate and trade-offs

IaC Templates

Terraform HCL: Production-ready Google provider configs
gcloud CLI: Scripted deployment commands
Cloud Build YAML: CI/CD pipeline definitions

Cost Analysis

Current spend breakdown with optimization recommendations
Priority action list (high/medium/low) and implementation checklist

Anti-Patterns

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

Cross-References

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

Reference Documentation

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

FAQ

What does the gcp-cloud-architect skill do?

How do I install gcp-cloud-architect?

Run npx skills add https://github.com/alirezarezvani/claude-skills --skill gcp-cloud-architect in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/gcp-cloud-architect/ (personal) or .claude/skills/gcp-cloud-architect/ (project).

Which AI agents can use gcp-cloud-architect?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is gcp-cloud-architect safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains gcp-cloud-architect?

It is published by alirezarezvani in alirezarezvani/claude-skills. It has 718 recorded installs.

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gcp-cloud-architect

Install

Install

Describe the task

Review the result

SKILL.md

GCP Cloud Architect

Workflow

Step 1: Gather Requirements

Step 2: Design Architecture

Step 3: Estimate Cost

Step 4: Generate IaC

Step 5: Configure CI/CD

Step 6: Security Review

Tools

architecture_designer.py

cost_optimizer.py

deployment_manager.py

Quick Start

Web App on Cloud Run (< $100/month)

Microservices on GKE ($500-2000/month)

Serverless Data Pipeline

ML Platform

Input Requirements

Output Formats

Architecture Design

IaC Templates

Cost Analysis

Anti-Patterns

Cross-References

Reference Documentation

FAQ

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