Skill

role-architect:gcp-architect

GCP architecture expertise including Google Cloud Architecture Framework, project and organization structure, VPC and networking design, data and analytics architecture, Kubernetes and serverless patterns, AI/ML platform design, security architecture, and cost optimization strategies. Use proactively when designing systems on GCP, evaluating GCP services, planning GCP organization structure, or architecting for GCP-specific capabilities.

Install

npx claudepluginhub rnavarych/alpha-engineer --plugin role-architect

Tool Access

This skill is limited to using the following tools:

ReadGrepGlobBash

Preview

- Designing GCP organization, folder, and project structure for a new workload

Supporting Assets

references/gcp-data-aiml-security-cost.mdreferences/gcp-platform-and-compute.md

SKILL.md

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Stats

Parent Repo Stars9

Parent Repo Forks0

Last CommitMar 3, 2026

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role-architect:gcp-architect

When to use

Designing GCP organization, folder, and project structure for a new workload

Selecting between GKE Autopilot, GKE Standard, and Cloud Run for container workloads

Architecting Shared VPC, global load balancing, or hybrid connectivity with Cloud Interconnect

Choosing between BigQuery, Spanner, AlloyDB, Firestore, and Bigtable for data storage

Designing Vertex AI training and serving pipelines or generative AI architectures

Configuring BeyondCorp, VPC Service Controls, or Security Command Center

Optimizing costs with Committed Use Discounts, Sustained Use Discounts, and FinOps practices

Core principles

Shared VPC by default — centralize network management, decentralize compute

Organization policies as guardrails — enforce at folder level before projects are created

Workload Identity everywhere — no service account keys on compute resources

Global load balancing — GCP's network is global; use it for traffic management

CUDs before Spot — commit to steady-state baseline first, then use Spot for burst

Reference Files

references/gcp-platform-and-compute.md — Architecture Framework five pillars, organization/project structure, Landing Zone, Shared VPC design, VPC Flow Logs, global load balancing, Cloud Armor, Cloud Interconnect, GKE Autopilot vs Standard, multi-cluster Fleet, Cloud Run, and Compute Engine MIGs

references/gcp-data-aiml-security-cost.md — BigQuery architecture patterns, Pub/Sub and Dataflow streaming pipeline, Cloud Spanner and AlloyDB, Firestore and Bigtable, Vertex AI training and serving, TPU architecture, generative AI with Vector Search; BeyondCorp and IAP, VPC Service Controls, Security Command Center, Cloud KMS and DLP; CUDs, Sustained Use Discounts, and FinOps with billing export to BigQuery

GCP Architect

When to use

Designing GCP organization, folder, and project structure for a new workload
Selecting between GKE Autopilot, GKE Standard, and Cloud Run for container workloads
Architecting Shared VPC, global load balancing, or hybrid connectivity with Cloud Interconnect
Choosing between BigQuery, Spanner, AlloyDB, Firestore, and Bigtable for data storage
Designing Vertex AI training and serving pipelines or generative AI architectures
Configuring BeyondCorp, VPC Service Controls, or Security Command Center
Optimizing costs with Committed Use Discounts, Sustained Use Discounts, and FinOps practices

Core principles

Shared VPC by default — centralize network management, decentralize compute
Organization policies as guardrails — enforce at folder level before projects are created
Workload Identity everywhere — no service account keys on compute resources
Global load balancing — GCP's network is global; use it for traffic management
CUDs before Spot — commit to steady-state baseline first, then use Spot for burst

Reference Files

references/gcp-platform-and-compute.md — Architecture Framework five pillars, organization/project structure, Landing Zone, Shared VPC design, VPC Flow Logs, global load balancing, Cloud Armor, Cloud Interconnect, GKE Autopilot vs Standard, multi-cluster Fleet, Cloud Run, and Compute Engine MIGs
references/gcp-data-aiml-security-cost.md — BigQuery architecture patterns, Pub/Sub and Dataflow streaming pipeline, Cloud Spanner and AlloyDB, Firestore and Bigtable, Vertex AI training and serving, TPU architecture, generative AI with Vector Search; BeyondCorp and IAP, VPC Service Controls, Security Command Center, Cloud KMS and DLP; CUDs, Sustained Use Discounts, and FinOps with billing export to BigQuery