What does this skill do, and when should you use it?
This skill is designed for specific, complex workloads that require holistic guidance across multiple Google Cloud products. It begins by collecting functional, non-functional, current-state, and dependency requirements, then produces a technical decomposition, product choices, architecture diagram, architecture description, design guidance, and deployment guidance. The workflow requires explicit approval at key transitions and includes pre-deployment static validation plus optional runtime validation. Product-specific and Google Cloud recipe skills are preferred when they directly address the workload.
It interactively gathers workload requirements, identifies ambiguities or contradictions, and requests clarification before architectural recommendations continue. It then generates a technical decomposition, Google Cloud product and feature recommendations, a Mermaid architecture diagram, an architecture description, design recommendations, and deployment guidance. It grounds Phase 2 content with the Google Developer Knowledge MCP server, relevant skills, and official Google Cloud documentation, adding citations to generated guidance. It also creates and runs pre-deployment dry-run and static policy checks, can provide runtime verification commands after deployment, and can consolidate the output into solution-architecture-guide.md before writing code files with user permission.
- A cloud architect needs a multi-product Google Cloud design for a workload currently running on-premises or with another cloud provider.
- A platform engineering team needs structured discovery of security, reliability, cost, performance, operations, and sustainability requirements before design begins.
- A technical lead wants approval checkpoints for product selection, diagrams, architecture descriptions, recommendations, and deployment guidance.
- A cloud team needs dry-run, network-topology, firewall, and IAM checks before provisioning live resources.
How do you install this skill?
- The core workflow depends on the Google Developer Knowledge MCP, Google Cloud documentation, and tools such as gcloud or curl, without defined fallbacks or failure feedback for service reachability, quotas, authentication, or documentation changes.
- The skill can generate and, after approval, write deployment code, but it does not specify handling, redaction, permissions, or rollback for credentials, secrets, personal data, or sensitive architecture information.
- No verified example outputs, automated tests, or CI evidence are provided; deployment guidance and product-status decisions require human review.
- Chinese-language quality and availability of Google Cloud and related MCP services from mainland-China networks are not documented.
- Shell / CLI
- Network access
- Local filesystem
- MCP Server
Google Developer Knowledge MCP server
Run:
npx skills add google/skills
Select google-cloud-solution-architecture during installation. The repository is a collection of 90 skills; the source does not specify a separate version requirement or dedicated installation command.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/google/skills.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/skills/cloud/google-cloud-solution-architecture" ~/.claude/skills/
rm -rf "$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?
Once installed, send your agent any of these to trigger it:
- Discover requirements and design an end-to-end Google Cloud architecture for a complex workload migrating from on-premises.
After installation, submit a request for a specific complex, multi-product Google Cloud workload, for example: “Discover requirements and design an end-to-end Google Cloud architecture for a complex workload migrating from on-premises.” The skill begins with questions and does not proceed to architecture design until requirements and the technical decomposition are approved. No dedicated trigger command is documented.
What are this skill's strengths and limitations?
- Covers requirements discovery, architecture design, validation, and packaging in one workflow.
- Explicitly handles conflicting requirements and requires approval before major deliverables and phase transitions.
- Includes source grounding, official documentation citations, pre-deployment dry-runs, and optional runtime validation.
- Fits concrete workloads requiring holistic design across multiple Google Cloud products.
- It is not intended for workloads directly covered by a specialized product skill or Google Cloud recipe skill.
- It depends on access to the Google Developer Knowledge MCP server, relevant skills, and official documentation resources.
- Runtime validation depends on the user deploying the infrastructure; the source provides no fixed deployment implementation or test suite.
- It says deployment and code artifacts can be generated, but does not specify a programming language, IaC tool, or supported platform matrix.
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 Solution Architect this page ✓ Google · Official | 57 · Use with care | ★ 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 Operational Excellence Advisor ✓ Google · Official | 53 · Use with care | ★ 21k | 12d ago | Apache-2.0 |
| Google Cloud gcloud CLI Safety Skill ✓ Google · Official | 51 · Use with care | ★ 21k | 12d ago | Apache-2.0 |
| Render Deployment Assistant ✓ OpenAI · Official | 49 · Use with care | ★ 28k | 4mo ago | — |
Compared with product-specific skills or google-cloud-recipe-* skills, this skill targets holistic architecture for complex multi-product workloads; use those specialized skills when they directly address the workload or use case.
How did FollowSkills review this skill?
The process requires approval of the technical decomposition, product choices, diagrams, descriptions, recommendations, and validation plan, and requests permission before validation execution or writing code. However, least-privilege permissions, sensitive-data handling, external data flows, dependency isolation, rollback, and recovery boundaries are not specified, so points are deducted.
The document defines ordered phases, ambiguity resolution, approval loops, and troubleshooting after validation issues, making the happy path coherent. It provides no committed test suite, CI coverage, pinned dependencies, reproducible procedures, or standardized diagnostic failures, and execution depends on undeclared tool availability, so points are deducted.
The skill clearly identifies holistic Google Cloud architecture use cases, non-fit cases for specialized or narrowly focused skills, required discovery inputs, and staged outputs. It does not declare Chinese-language support, mainland-China service reachability, or fallbacks when Google Cloud or MCP services are unavailable, and its trigger boundary remains broad, so points are deducted.
The documentation is well structured, uses progressive phases, includes an output template and official reference indexes, documents repository installation, states Apache-2.0 licensing, and provides README maintenance and issue-reporting paths. The skill itself lacks dependency setup details, compatibility/version policy, changelog, explicit maintenance ownership, and known limitations, so points are deducted.
The stated workflow covers requirements discovery, architecture design, deployment guidance, validation, and packaging, with intended outputs including a usable architecture document, Mermaid diagram, and deployment guidance. Static source material does not verify correctness, completeness, deployability, or marginal benefit over manual work, so the score is capped by static calibration and reduced for missing evidence.
The skill requires citations to official Google Cloud documentation and supplies architecture, decision-making, and best-practice reference indexes plus a fixed output template, providing some auditability. There are no test results, CI execution records, representative verified outputs, or independent corroboration, so the score reaches the static ceiling but not beyond it.
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 →