gcp-cloud-architect
by alirezarezvanigcp-cloud-architect helps design Google Cloud architectures, choose Cloud Run, GKE, Cloud SQL, BigQuery, and other GCP services, estimate cost tradeoffs, and generate deployment-oriented guidance using included references and scripts.
This skill scores 82/100, making it a solid listing candidate for directory users who want an agent to design Google Cloud architectures with less guesswork than a generic prompt. The repository provides a clear trigger description, a requirements-driven workflow, runnable helper scripts, and reference guides for architecture patterns, service selection, and best practices. Users should still treat generated deployment and cost outputs as starting points that require review before production use.
- Clear trigger scope for GCP architecture design, Cloud Run, GKE, BigQuery pipelines, cost optimization, and migration scenarios.
- Operational workflow starts with concrete requirement gathering and uses scripts such as architecture_designer.py, cost_optimizer.py, and deployment_manager.py.
- Reference materials include architecture pattern matrices, service selection guidance, naming/IAM/networking practices, and cost/disaster recovery considerations.
- No install command or README is provided at the skill path, so adoption depends on users already understanding the repository’s skill installation conventions.
- Generated gcloud/Terraform-style outputs and cost estimates appear template-based and should be validated against current GCP pricing, quotas, security requirements, and production constraints.
Overview of gcp-cloud-architect skill
What gcp-cloud-architect is for
gcp-cloud-architect is a Claude skill for designing practical Google Cloud architectures, choosing between GCP services, estimating cost directionally, and generating deployment-oriented guidance. It is best suited for users who need a first-pass architecture for Cloud Run, GKE, Cloud Functions, Cloud SQL, BigQuery, Firestore, data pipelines, migrations, or cost optimization rather than a generic “design me a cloud app” answer.
Best-fit users and projects
The gcp-cloud-architect skill fits startup MVPs, SaaS platforms, mobile backends, enterprise microservices, analytics pipelines, and teams comparing GCP deployment options. It is especially useful when you know the application shape but need help translating requirements into a service stack, architecture pattern, cost tradeoff, and implementation plan. It is less useful if you need provider-neutral architecture, AWS/Azure guidance, or a fully audited production design without human cloud review.
What makes it different from a normal prompt
The skill includes structured decision support beyond a plain prompt: references/architecture_patterns.md for pattern selection, references/service_selection.md for choosing compute, database, storage, messaging, networking, and identity services, and references/best_practices.md for naming, labels, IAM, monitoring, cost, and disaster recovery. It also includes Python helper scripts for architecture recommendation, cost optimization, and deployment script generation.
Important adoption considerations
Treat outputs as design accelerators, not final architecture authority. GCP pricing, quotas, regional availability, compliance requirements, and organization policies change frequently. The skill can produce stronger architecture drafts when supplied with traffic, budget, team skill level, compliance needs, data size, RPO/RTO, and operational constraints. Without those inputs, it will fall back to broad defaults that may understate cost or complexity.
How to Use gcp-cloud-architect skill
gcp-cloud-architect install context
Install the skill in your Claude skills environment with:
npx skills add alirezarezvani/claude-skills --skill gcp-cloud-architect
The upstream skill lives at engineering-team/skills/gcp-cloud-architect. After install, read SKILL.md first, then inspect:
references/architecture_patterns.mdreferences/service_selection.mdreferences/best_practices.mdscripts/architecture_designer.pyscripts/cost_optimizer.pyscripts/deployment_manager.py
These files explain the decision model and show what the helper scripts expect as input.
Inputs the skill needs for useful architecture
For reliable gcp-cloud-architect usage, provide requirements in concrete terms. Good inputs include:
{
"application_type": "saas_platform",
"expected_users": 25000,
"requests_per_second": 120,
"budget_monthly_usd": 1500,
"team_size": 5,
"gcp_experience": "intermediate",
"compliance": ["SOC 2", "GDPR"],
"data_size_gb": 500,
"availability": "99.9%",
"rpo": "15 minutes",
"rto": "1 hour",
"region": "europe-west1"
}
This prevents the model from guessing between Cloud Run, GKE, Cloud SQL, Firestore, Pub/Sub, BigQuery, and multi-region designs.
Strong prompt pattern for Cloud Architecture
A weak prompt is: “Design a GCP architecture for my app.”
A stronger prompt is:
Use the gcp-cloud-architect skill for Cloud Architecture. Design a GCP architecture for a B2B SaaS API with 25K users, 120 RPS peak, PostgreSQL data model, SOC 2 and GDPR needs, EU hosting preference, monthly budget under $1,500, five engineers, intermediate GCP experience, 99.9% availability, RPO 15 minutes, and RTO 1 hour. Compare Cloud Run and GKE, recommend storage and database choices, estimate major cost drivers, list risks, and produce a phased deployment plan.
This gives the skill enough context to select a pattern, explain tradeoffs, and avoid over-engineering.
Practical workflow with scripts and references
Use the skill in four passes:
- Requirements pass: Ask it to identify missing architecture inputs before designing.
- Pattern pass: Use
architecture_patterns.mdand, if working locally, runpython scripts/architecture_designer.py --input requirements.json. - Cost pass: Ask for cost drivers and use
cost_optimizer.pywhen you have current resources and spend. - Deployment pass: Use
deployment_manager.pyonly after validating the pattern, project ID, region, IAM assumptions, and networking model.
Before using generated gcloud or Terraform-style output, review security, billing, IAM, and organization policy settings manually.
gcp-cloud-architect skill FAQ
Is gcp-cloud-architect beginner-friendly?
Yes, if you provide clear requirements and ask for explanations. The service selection reference explains when to use Cloud Run, Cloud Functions, GKE Autopilot, Compute Engine, Cloud SQL, Firestore, BigQuery, Pub/Sub, and related services. Beginners should ask for “why this service, why not alternatives, and what to monitor first” to avoid copying a design they do not understand.
When should I not use this skill?
Do not use gcp-cloud-architect as the only source for regulated production systems, security architecture sign-off, exact pricing commitments, or live migration planning. It is also not ideal for multi-cloud comparison, AWS-first teams, or organizations with heavy custom platform constraints unless you provide those constraints explicitly.
How is this better than asking Claude directly?
A direct prompt can produce a plausible cloud design, but the gcp-cloud-architect skill anchors the answer in a repeatable GCP-specific workflow: requirement gathering, architecture pattern selection, service choice, cost estimation, best practices, and deployment script generation. The references and scripts reduce guesswork and make outputs easier to audit.
Does it generate deployable infrastructure?
It can help generate gcloud scripts and Terraform-oriented configuration through scripts/deployment_manager.py, but you should treat this as a starter scaffold. Before deployment, verify project IDs, regions, APIs, service accounts, IAM roles, VPC design, secrets handling, logging, backup, and budget alerts.
How to Improve gcp-cloud-architect skill
Improve gcp-cloud-architect results with better constraints
The fastest way to improve gcp-cloud-architect output is to provide constraints that force real tradeoffs. Include monthly budget, user count, peak RPS, latency target, data residency, compliance, team size, GCP experience, database preference, deployment frequency, and operational tolerance. “Use managed services where possible” or “avoid Kubernetes unless justified” are especially useful constraints.
Common failure modes to watch for
Common issues include choosing GKE when Cloud Run would be simpler, ignoring egress and BigQuery scan costs, under-specifying IAM, assuming multi-region is necessary, or generating deployment steps before the architecture is validated. Ask the skill to list assumptions, rejected alternatives, and cost-sensitive components so these problems are visible early.
Iterate from draft to production-ready design
After the first design, ask for a second pass focused on gaps:
- “Review this architecture for cost risks under variable traffic.”
- “Identify IAM roles and least-privilege service accounts.”
- “Convert this into dev, staging, and prod environments.”
- “Add observability, backup, disaster recovery, and rollback steps.”
- “Show what changes if traffic grows 10x.”
This turns the skill from a one-shot diagram generator into an architecture review partner.
Add project-specific context before deployment
Before using generated scripts, add your real project ID, billing constraints, organization policies, preferred regions, naming convention, labels, CI/CD tool, secret management approach, and existing network layout. The skill’s best_practices.md includes naming, labels, IAM, monitoring, cost optimization, and disaster recovery guidance; aligning those with your environment will produce safer and more maintainable Cloud Architecture outputs.