cloud-gcp-architect_skill

This skill helps design comprehensive GCP multi-service architectures across compute, storage, networking with cost, security, and compliance optimization.

Python

5

GitHub Stars

1

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill williamzujkowski/cognitive-toolworks --skill cloud-gcp-architect

SKILL.md28.0 KB

Overview

This skill helps design multi-service Google Cloud Platform (GCP) architectures across compute, storage, networking, and serverless with a focus on cost optimization, security hardening, and alignment to the GCP Architecture Framework. It delivers quick recommendations (T1) for common workloads and detailed, production-ready designs with Terraform IaC, cost estimates, and migration plans (T2). The output is pragmatic: service selections, justification, estimates, security baselines, and next steps.

How this skill works

Provide workload characteristics, requirements (performance, security, cost, or compliance), deployment tier (T1 or T2), and target regions. For T1 it classifies the workload and returns a fast architecture recommendation with rough cost guidance and framework alignment. For T2 it expands the design into an end-to-end solution: service choices, integration patterns, security hardening, Terraform modules, detailed cost projections, and a migration or deployment plan.

When to use it

Designing a multi-service GCP solution spanning compute, storage, and networking
Assessing or optimizing an architecture against the GCP Architecture Framework
Preparing a migration from on-prem or another cloud to GCP
Creating Terraform-based production IaC and deployment plans
Choosing between serverless, containers, and VMs with cost/security trade-offs

Best practices

Classify workload type first (web-app, data-processing, real-time, batch, machine-learning, hybrid)
Apply least-privilege IAM and service accounts; avoid broad basic roles
Use multi-zone or multi-region designs for critical workloads and health-checked load balancers
Leverage lifecycle policies and committed use discounts for cost optimization
Modularize Terraform (network, compute, storage, database) and use remote state with locking

Example use cases

Quickly recommend Cloud Run + Cloud SQL for a stateless web app with auto-scaling and minimal ops
Design a data-processing pipeline using Cloud Storage, Pub/Sub, Dataflow, and BigQuery with lifecycle rules
Prepare a machine learning inference platform using Vertex AI, Cloud Storage, and Cloud Run for model serving
Create a migration plan: discovery, wave-based cutover, DNS traffic shifting, and rollback steps
Produce Terraform modules for a multi-region VPC, private subnets, and Cloud SQL with private IP

FAQ

Supply workload_type, at least one requirement (performance, security, cost, or compliance), deployment_tier (T1 or T2), and regions if multi-region design is needed.

When should I choose Cloud Run vs GKE vs Compute Engine?

Use Cloud Run for containerized HTTP services with automatic scaling, GKE for complex Kubernetes orchestration and portability, and Compute Engine when full OS control or specialized machine types are required.