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gcp-cloud-architect

Design and operate GCP: architectures with Terraform IaC, Cloud Run, BigQuery pipelines, cost optimization and Cloud Build CI/CD, plus day-2 operations for IAM and Secret Manager, Cloud Run, functions, Cloud SQL, Compute Engine, networking and audit logs.

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SebastianLuser/claude-code-arcane3 stars(GitHub 3 + cccskills 0)

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See how to install

Files included (14)

  • SKILL.md6.0 KB
  • references/architecture_patterns.md17.6 KB
  • references/best_practices.md12.4 KB
  • references/ops-audit-logs.md2.0 KB
  • references/ops-cloud-functions.md1.9 KB
  • references/ops-cloud-run.md2.6 KB
  • references/ops-cloud-sql.md2.3 KB
  • references/ops-compute.md1.7 KB
  • references/ops-iam-secrets.md2.8 KB
  • references/ops-networking.md1.9 KB
  • references/service_selection.md13.7 KB
  • scripts/architecture_designer.py34.0 KB
  • scripts/cost_optimizer.py19.7 KB
  • scripts/deployment_manager.py23.4 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

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

Select from recommended patterns:

  • Serverless Web: Cloud Storage + Cloud CDN + Cloud Run + Firestore
  • Microservices on GKE: GKE Autopilot + Cloud SQL + Memorystore + 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 with service stacks, Terraform configs, and cost estimates.

Validation checkpoint: Confirm the recommended pattern matches the team's operational maturity and compliance requirements before proceeding.

Step 3: Estimate Cost

python scripts/cost_optimizer.py --resources current_setup.json --monthly-spend 2000

Output includes monthly cost breakdown, right-sizing recommendations, committed use discount opportunities, and sustained use discount analysis. Use the GCP Pricing Calculator for detailed estimates.

Step 4: Generate IaC

python scripts/deployment_manager.py --app-name my-app --pattern serverless_web --region us-central1

Generates production-ready Terraform HCL and gcloud CLI scripts. See references/architecture_patterns.md for full template examples including Cloud CDN, Identity Platform, IAM, and Cloud Monitoring.

Step 5: Configure CI/CD

Set up automated deployment with Cloud Build or GitHub Actions. See references/best_practices.md for cloudbuild.yaml templates and trigger configuration.

Step 6: Security Review

Verify security configuration:

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

Tools

architecture_designer.py

Recommends GCP services based on workload requirements.

python scripts/architecture_designer.py --input requirements.json --output design.json

cost_optimizer.py

Analyzes GCP resources for cost savings: idle removal, right-sizing, committed use discounts, storage transitions.

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

deployment_manager.py

Generates gcloud CLI scripts and Terraform configurations.

python scripts/deployment_manager.py --app-name my-app --pattern serverless_web --region us-central1

Quick Start

ScenarioArchitectureEstimated Cost
Web App on Cloud Run (<1k users)Cloud Run + Firestore + Identity Platform + Cloud CDN$15-40/mo
Microservices SaaS (50k users)GKE Autopilot + Cloud SQL + Memorystore + Cloud Build$500-2000/mo
Serverless Data PipelinePub/Sub + Dataflow + BigQuery + LookerVariable
ML PlatformVertex AI + Cloud Storage + BigQuery + Cloud FunctionsVariable

Anti-Patterns

Anti-PatternBetter Approach
Using default VPC for productionCustom VPC with private subnets
Over-provisioning GKE node poolsGKE Autopilot or cluster autoscaler
Storing secrets in env varsSecret Manager with Workload Identity
Single-region deployment for SaaSMulti-region with Cloud Load Balancing
BigQuery on-demand for heavy workloadsBigQuery slots (flat-rate) for consistency
Cloud Functions for long tasks (>60s)Cloud Run for longer-running tasks

Operate

Once it runs, read only the file for the service at hand. Engine-level database work is in /database-ops, backups in /backup-strategy, rollback with revisions in /rollback-strategy, incidents in /incident-response.

AreaReferenceCovers
IAM and secretsreferences/ops-iam-secrets.mdOne service account per service, GitHub through Workload Identity Federation, Secret Manager access and rotation, auditing
Cloud Runreferences/ops-cloud-run.mdConcurrency, min/max instances, billing mode, probes, ingress and VPC egress, deploys, debugging
Cloud Run functionsreferences/ops-cloud-functions.mdDeploys, Eventarc triggers and idempotency, retries and dead letters, scheduled calls, debugging
Cloud SQLreferences/ops-cloud-sql.mdPrivate IP and the Auth Proxy, IAM auth, HA, maintenance, restores, alerts
Compute Enginereferences/ops-compute.mdRegional MIGs with autohealing, OS Login and IAP SSH, snapshots, debugging
Networkingreferences/ops-networking.mdCustom VPC, firewall by service account, Cloud NAT, private access, load balancer with Cloud Armor, Connectivity Tests
Audit logsreferences/ops-audit-logs.mdLog types and defaults, Data Access logs, sinks, investigation queries, alerts

Reference Documentation

DocumentContents
references/architecture_patterns.md6 patterns with Terraform configs, gcloud scripts, cost estimates
references/service_selection.mdDecision matrices for compute, database, storage, messaging
references/best_practices.mdNaming, labels, IAM, networking, monitoring, DR

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