What does this skill do, and when should you use it?
This skill supports secure n-tier serverless web applications built on Google Cloud. It uses Cloud Run for presentation and internal application tiers, Cloud SQL for PostgreSQL for data, and optionally Memorystore for Redis. Its workflow covers requirements discovery, solution design, Terraform and gcloud generation, iterative implementation, and validation. It is not intended for VM- or GKE-based architectures or non-Google Cloud environments.
It analyzes workload requirements, asks up to two optional clarification questions, confirms a default three-tier architecture, and uses guidance and templates from the references/ and assets/ directories. It then produces product mappings, a Mermaid architecture diagram, Terraform, gcloud CLI deployment commands, deployment guidance, and a tailored validation script, with the architecture report saved using an ISO 8601 UTC timestamp and a .md extension. Validation covers SSL, ingress isolation, load-balancer access, Cloud Armor WAF behavior, private service connectivity, logs, and database telemetry.
- A Google Cloud team designing a Cloud Run frontend, private application services, and a Cloud SQL database.
- A platform engineer configuring an external Application Load Balancer with Cloud Armor and optional Cloud CDN for a public web entry point.
- A security architect isolating application tiers with VPC networking, Direct VPC Egress, Private Service Connect, and private DNS.
- A development team that wants Terraform plus a no-Terraform gcloud deployment path for the same architecture.
- An operations team creating post-deployment checks for SSL, ingress controls, WAF behavior, and private connectivity.
How do you install this skill?
- Do not treat the supplied template as a deployable Terraform or gcloud package; first provide and review the missing assets/main.tf, complete commands, and validation script.
- Deployment creates external infrastructure changes; explicitly confirm the project, resource scope, permissions, and rollback or retention policy before running Terraform or gcloud.
- Separately verify the reachability and compliance of Google Cloud services, the optional MCP dependency, and Cloud Shell in the intended mainland-China network environment.
- Shell / CLI
- Network access
- Local filesystem
- MCP Server
Node.jsTerraformGoogle Cloud CLI
Run npx skills add google/skills, then select skills/cloud/google-cloud-solution-n-tier-serverless-web-app from the repository. The source does not document client-specific installation procedures beyond this command.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/google/skills.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/skills/cloud/google-cloud-solution-n-tier-serverless-web-app" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
In an Agent Skills-compatible client with the skill loaded, use a prompt such as: Design a secure three-tier serverless web application on Google Cloud using Cloud Run and Cloud SQL for PostgreSQL, and generate Terraform, gcloud deployment commands, and a validation script. If requirements are incomplete, the skill follows its default configuration and may ask up to two optional questions. The source does not specify a client-specific trigger syntax.
What are this skill's strengths and limitations?
- Covers requirements discovery, architecture, IaC, deployment guidance, and validation in one workflow.
- Provides explicit guidance for isolating Cloud Run application tiers, private data services, and least-privilege egress.
- Addresses both Terraform and self-contained gcloud CLI implementation paths.
- Includes tailored validation for SSL, ingress, WAF, and private service connectivity.
- Its scope is limited to n-tier serverless web applications on Google Cloud.
- It does not target VM- or GKE-based architectures.
- The source provides no test suite, deployment results, or platform compatibility test evidence.
- Deployment still requires Terraform, gcloud, IAM, networking, and Google Cloud resource configuration.
- Cost estimates and a complete least-privilege permission matrix are not provided, and some guidance depends on referenced files not included in the source excerpt.
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| Google Cloud Secure Serverless Tiers this page ✓ Google · Official | 44 · Not recommended | ★ 21k | 12d ago | Apache-2.0 |
| Google Cloud AI Agent Builder ✓ Google · Official | 53 · Use with care | ★ 21k | 12d ago | Apache-2.0 |
| Google Cloud Global Load Balancer Configurator ✓ Google · Official | 45 · Not recommended | ★ 21k | 12d ago | Apache-2.0 |
| Google Cloud Live Multimodal Streaming Architect ✓ Google · Official | 55 · Use with care | ★ 21k | 12d ago | Apache-2.0 |
| Google Cloud WAF Security Advisor ✓ Google · Official | 54 · Use with care | ★ 21k | 12d ago | Apache-2.0 |
The skill explicitly excludes VM- and GKE-based architectures. Compared with those alternatives, it focuses on Cloud Run-based multi-tier serverless design with Cloud SQL for PostgreSQL as the data tier.
How did FollowSkills review this skill?
The document specifies restricted frontend and backend ingress, private networking, default-deny egress controls, Secret Manager, IAM database authentication, VPC Service Controls, and confirmation before deployment, making the main security boundaries and data flows visible. Points are deducted because required permissions are described broadly as Project Editor, Security Admin, and similar roles, without a complete least-privilege matrix, sensitive-data classification, rollback procedure, or per-operation confirmation for Terraform and gcloud changes.
The workflow is clearly phased and includes abnormal-path validation targets and diagnostic checks. Points are deducted because the referenced assets/main.tf is absent from the supplied material, while the Terraform, gcloud commands, and validation script in the template remain placeholders; key paths therefore cannot be statically reproduced and dependency and failure-feedback coverage is thin.
The audience, intended scenario, and exclusions for VM, GKE, and non-Google-Cloud designs are explicit, with global versus regional load-balancer and optional Redis choices. Points are deducted because the input/output contract, non-fit business ranges, and trigger conditions remain broad; Chinese-language interaction is not specified, and reachability of core Google Cloud functions from mainland-China networks is not evidenced.
The skill is organized progressively across requirements, design, implementation, and validation, with named parameters, limitation warnings, example templates, internal references, and Apache-2.0 metadata. Points are deducted because the referenced assets/main.tf is not supplied and the template contains many unfilled placeholders; the skill has no clear versioning policy, changelog, named maintenance owner, or update path.
The intended workflow covers requirements discovery, architecture, IaC, deployment, and validation, and explicitly targets an architecture report, Terraform, gcloud commands, and a validation script. Points are deducted because the supplied output template contains neither deployable code nor an executable validation script, and completion depends on user-provided images, domains, project permissions, and later manual approval; static evidence supports only limited task completion.
The skill supplies architectural rules, an audit checklist, technical guidance, and validation steps, so some design claims are traceable to internal reference material. Points are deducted because there is no committed test suite, CI workflow, execution result, or third-party reproduction evidence; the absent main.tf also prevents independent verification of the key implementation.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →