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Official agent skill

Google Cloud Solution Architecture

by google in google/skills

Interactively discovers requirements and designs holistic, multi-product system architectures, solution blueprints, and deployment recommendations for complex workloads on Google Cloud.

OfficialApache-2.0Auto-check passedDevelopment

Install Google Cloud Solution Architecture

skills CLI
$ npx skills add google/skills --skill google-cloud-solution-architecture -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install google/skills google-cloud-solution-architecture --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/cloud/google-cloud-solution-architecture .claude/skills/google-cloud-solution-architecture && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
google-cloud-solution-architecture
GitHub stars
21k
Token cost
~3.5k tokens
SKILL.md length
1,596 words
Files
5 (incl. references, assets)
Skills in repo
145
Repo updated
First seen
Licence
Apache-2.0

At a glance

Interactively discovers requirements and designs holistic, multi-product system architectures, solution blueprints, and deployment recommendations for complex workloads on Google Cloud.

  • Works in 4 steps: Requirements discovery → Solution architecture → Solution validation → …
  • Designing end-to-end cloud solutions
  • SKILL.md covers Overview of the workflow, Phase 1: Requirements discovery, Phase 2: Solution architecture and Phase 3: Solution validation, plus 1 more section
  • Calls terraform and gcloud

What it does

Google Cloud Solution Architecture is an agent skill from google/skills, published by the product's own GitHub organization. Interactively discovers requirements and designs holistic, multi-product system architectures, solution blueprints, and deployment recommendations for complex workloads on Google Cloud. Use when designing end-to-end cloud solutions, selecting and integrating Google Cloud services, generating architecture diagrams, or conducting requirements discovery for new cloud workloads or migrations. Don't use for single-product tasks (use product-specific skills), initial onboarding or authentication (use…

Its SKILL.md is about 3.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including reference files and assets (for example `assets/output-template.md`, `references/architecture-guides.md` and `references/best-practices-guides.md`).

It sits in Development, covering Software architecture, Diagrams and Cloud architecture. It works with Google Cloud. The repository describes itself as: Agent Skills for Google products and technologies. The licence is Apache-2.0.

When your agent uses it

  • Designing end-to-end cloud solutions
  • Selecting and integrating Google Cloud services
  • Generating architecture diagrams
  • Conducting requirements discovery for new cloud workloads

Example prompts

  • “/google-cloud-solution-architecture”

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Requirements discovery
  2. Solution architecture
  3. Solution validation
  4. Solution packaging and presentation

What it can do on your machine

Read from SKILL.md and the folder at commit 8a1ac05. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • terraform
    • gcloud

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • developerknowledge.googleapis.com
    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Google Cloud Solution Architecture loads about 3.5k tokens when it runs, and up to ~33k if it reads all its reference files. Until then it costs about 171 tokens; SKILL.md has 1,596 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~171
When it runs · the whole SKILL.md, loaded when a task matches
~3.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~33k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from google/skills at commit 8a1ac05, republished under its Apache-2.0 licence (© google). 1,596 words, ~3,477 tokens.

Download SKILL.mdSave it as .claude/skills/google-cloud-solution-architecture/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
google-cloud-solution-architecture
description
Interactively discovers requirements and designs holistic, multi-product system architectures, solution blueprints, and deployment recommendations for complex workloads on Google Cloud. Use when designing end-to-end cloud solutions, selecting and integrating Google Cloud services, generating architecture diagrams, or conducting requirements discovery for new cloud workloads or migrations. Don't use for single-product tasks (use product-specific skills), initial onboarding or authentication (use google-cloud-recipe-*), Well-Architected Framework reviews or audits (use google-cloud-waf-*), or workloads covered by specialized solution skills.
metadata.version
1.0.3
metadata.category
MultiProductSolutions

Google Cloud solution-architecture workflow

Overview of the workflow

The workflow consists of the following phases:

  • Phase 1: Requirements discovery. Gather detailed requirements related to the cloud workload or use case that the user needs assistance for.
  • Phase 2: Solution architecture. Use the requirements that were gathered in Phase 1 to generate a detailed solution architecture for the cloud workload or use case.
  • Phase 3: Solution validation. Create a plan to validate the generated solution, generate validation instructions and scripts, and provide them to the user to execute (or perform a dry-run validation with explicit permission from the user).
  • Phase 4: Solution packing and presentation. Consolidate the generated content and present the solution.

Important notes about the workflow:

  • Strict phase separation: During Phase 1 (Requirements discovery), when you ask the user clarifying questions, don't recommend, propose, or outline any architectural designs, technical decompositions, cloud services, or component mappings. Proposing solutions before functional and non-functional requirements are thoroughly assessed causes confirmation bias and risks anchoring the solution on specific products, features, or tools prematurely.

  • Iterative approval & task transitions: For each deliverable in this workflow (technical decompositions, product recommendations, diagrams, architectural descriptions, and deployment scripts), explicitly present your output to the user for approval. If the user requests modifications, iteratively revise the content until approved before progressing to the subsequent task or phase.

  • No autonomous execution of code and scripts: Don't run any scripts or code that you generate without explicit, unambiguous permission from the user. Executing scripts autonomously can provision unintended cloud resources (incurring unexpected costs), mutate live infrastructure, or pose security and safety risks. Always offer the option for the user to execute the commands manually.

  • When you can skip certain phases: If the user's prompt indicates that a specific phase or task in this workflow is already completed or approved (e.g., "requirements discovery stage is completed", "product selection is approved", or "architecture is confirmed"), don't repeat that phase or task. Instead, skip directly to the requested task (such as generating the technical decomposition, recommending products, or compiling the solution guide).

Phase 1: Requirements discovery

  1. Gather the following requirements related to the workload or use case for which the user needs assistance.

    CRITICAL: You MUST NOT generate any architecture designs, product recommendations, or technical decompositions until the user provides these requirements.

    • Functional requirements: Ask the user to describe the business processes, activities, and use cases of their workload.
    • Non-functional requirements: Ask the user to describe requirements for security, privacy, compliance, reliability, disaster recovery, cost, operations, performance, and sustainability.
      • CRITICAL: If non-functional requirements are missing or incomplete, you MUST ask the user to describe the requirements and explicitly explain why they are important (e.g., because they directly dictate operational SLAs, availability tiers, scaling configuration, cost budgets, resource types, and the security posture) when asking the user to supply them.
    • Current state: Ask whether the workload currently runs on other cloud providers or on-premises (if yes, prompt for the architecture of the existing deployment).
    • System dependencies: Ask the user to describe any dependencies between their application and other workloads, products, systems, or tools.

  2. Review the input that the user has provided so far, and check whether there are any ambiguities or contradictions (e.g., conflicting goals like complete network isolation with zero internet exposure vs. real-time ingestion from public APIs).

    If you identify any ambiguities or contradictions in the user's requirements, you must:

    • Clearly describe the ambiguities and contradictions.
    • Explain why the contradictory requirements cannot be simultaneously satisfied.
    • Request the user to clarify their trade-off preferences and choices to resolve the ambiguities and contradictions.
      • If the user delegates the choice to you (e.g., the user replies with "do what you think is best" or "you decide"), then provide a clear suggestion to resolve the ambiguity or contradiction, explain your reasoning, and ask the user to approve your suggestion.

    CRITICAL: Until all the ambiguities and contradictions that you identify are resolved, don't recommend or generate any architecture design, technical decomposition, or Google Cloud product recommendations. Ambiguous or contradictory requirements lead to invalid architectural assumptions.

  3. Generate a technical decomposition of the components of the workload that breaks down the solution into logical components. Present it to the user and obtain approval before proceeding to Phase 2.

    CRITICAL: Before proceeding to Phase 2, ensure that the user has approved the technical decomposition. Misalignment of the technical decomposition with the user's requirements will invalidate the outputs of the subsequent phases in this workflow.

Phase 2: Solution architecture

Use the approved requirements from Phase 1 to generate a comprehensive solution architecture.

Ground all generated content

For each task in this phase, to ensure that the generated content aligns with the latest and official Google Cloud guidance, you must ground the generated content by using the following resources:

  • Google Developer Knowledge MCP server
  • Relevant skills from https://github.com/google/skills
  • Official Google Cloud documentation, including the following:
    • Reference architectures and design guides that are relevant to the technology category of the workload: references/architecture-guides.md
    • Decision-making guides for the products and topics that are relevant to the workload: references/decision-making-guides.md
    • Best-practices guides for the products and topics that are relevant to the workload: references/best-practices-guides.md

For each item in the generated guidance, you must include citations to the relevant official Google Cloud documentation pages.

Show full SKILL.md (720 more words)Show less
Task 2.1: Identify Google Cloud products and features required for the workload.

  1. Recommend the products and features that are appropriate for each component of the user's workload.

    CRITICAL:

    • Don't recommend any products or features that are deprecated, retired, decommissioned, or unsupported. To check the status of a product or feature, call developerknowledge:answer_query or developerknowledge:search_documents with query strings like: "{product_name} release status".
    • If multiple products or features can be used for a component of the workload, then do the following:
      • Recommend the most appropriate product or feature. When alternative products exist, the relevant product documentation might provide guidance on when to choose each product. Follow that guidance.
      • Mention the available alternative products or features.
      • Explain the pros and cons of each alternative product or feature.

  2. Present the generated product recommendations to the user and ask whether any changes are needed.

    CRITICAL: Don't generate anything further (architecture diagrams, descriptions, or deployment configurations) in the same turn. Halt execution immediately after listing the product choices until the user approves the product selections.

  3. After the user approves the product selections, proceed to Task 2.2.

Task 2.2: Generate an architecture diagram.
  1. Generate an architecture diagram in Mermaid format: https://github.com/mermaid-js/mermaid.
  2. Present the generated diagram to the user and obtain approval before proceeding to Task 2.3.
Task 2.3: Generate an architecture description.
  1. Generate a description that explains the purpose of each component, the relationships between the components, and the task flow or data flow.
  2. Present the generated architecture description to the user and obtain approval before proceeding to Task 2.4.
Task 2.4: Generate design recommendations.

  1. Generate design recommendations and best practices to optimally configure each component in the architecture based on the workload's requirements.

    Important:

    • When generating design recommendations, incorporate the following:
      • Functional requirements that were gathered in Phase 1.
      • Non-functional requirements that were gathered in Phase 1.
    • To generate guidance for non-functional requirements, use the following resources:
      1. Best-practices guides for the products and topics that are relevant to the workload: references/best-practices-guides.md
      2. Well-Architected Framework pillar skills:
        • For security requirements: google-cloud-waf-security
        • For reliability requirements: google-cloud-waf-reliability
        • For cost optimization requirements: google-cloud-waf-cost-optimization
        • For operational excellence requirements: google-cloud-waf-operational-excellence
        • For performance optimization requirements: google-cloud-waf-performance-optimization
        • For sustainability requirements: google-cloud-waf-sustainability

  2. Present the generated recommendations to the user and obtain approval before proceeding to Task 2.5.

Task 2.5: Generate deployment guidance.
  1. Generate deployment guidance, including infrastructure-as-code and instructions to enable the user to deploy the solution.
  2. Present the generated deployment guidance to the user and obtain approval before proceeding to Phase 3.

Phase 3: Solution validation

Task 3.1: Pre-deployment validation
  1. Create a pre-deployment plan to statically validate the generated solution and verify that it meets the workload's requirements without provisioning live resources:
    • Deployment dry-run: Validate infrastructure syntax and preview the resources that will be provisioned using dry-run commands (e.g., terraform plan or (where supported) gcloud ... --dry-run).
    • Architecture & policy analysis: Perform static verification of network routing topologies, firewall rules, and IAM enforcement against best practices.
  2. Present the static validation plan to the user and obtain explicit permission from the user to execute the dry-run commands. If the user gives permission, then run the commands; otherwise, or if the user prefers, provide the exact commands that the user can run manually.
  3. Troubleshoot and fix any errors or policy discrepancies identified during dry-run checks until validation succeeds.
  4. Proceed to Task 3.2
Task 3.2: Runtime validation (Post-deployment)
  1. Ask the user whether they choose to deploy the infrastructure now to perform live runtime verification, or skip directly to Phase 4.
  2. If the user chooses to deploy the infrastructure:
    • After the user deploys the infrastructure, generate runtime verification commands (using tools like curl, ping, or gcloud) and provide them to the user to execute, to test live endpoint reachability, networking paths, and load balancer routing.
    • Troubleshoot any deployment or runtime routing issues until checks pass.
  3. Proceed to Phase 4.

Phase 4: Solution packaging and presentation

Package all the generated text and code artifacts for final presentation.

  1. Consolidate the text artifacts that were generated in Phase 2 and Phase 3 into a single Markdown file named solution-architecture-guide.md, based on the template in assets/output-template.md.
  2. Request the user's permission to write the code files in the user's workspace.
  3. After the user gives permission, write the code files in the user's workspace.

© google, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (references, assets) in skills/cloud/google-cloud-solution-architecture of google/skills.

  • SKILL.md
  • assets/output-template.md
  • references/architecture-guides.md
  • references/best-practices-guides.md
  • references/decision-making-guides.md

Open the folder on GitHubat commit 8a1ac05

Compare with similar skills

Google Cloud Solution Architecture next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Google Cloud Solution Architecture compared with similar skills
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Google Cloud Solution Architecture this skillgoogle/skills21k—~3.5kAutomated safety check: PassApache-2.0
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GCP DrawIO Diagram Generatora5c-ai/babysitter1.8k—~3.7kAutomated safety check: PassMIT
Drawio Azuresparklabx/drawio-ai-kit652—~1.6kAutomated safety check: PassMIT
AWS DrawIO Diagram Generatora5c-ai/babysitter1.8k—~4.2kAutomated safety check: PassMIT
Drawio GCPsparklabx/drawio-ai-kit652—~1.6kAutomated safety check: PassMIT

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Works with

Categories

Questions about Google Cloud Solution Architecture

What does Google Cloud Solution Architecture do?

Interactively discovers requirements and designs holistic, multi-product system architectures, solution blueprints, and deployment recommendations for complex workloads on Google Cloud. Google Cloud Solution Architecture is an agent skill from google/skills, published by the product's own GitHub organization. Interactively discovers requirements and designs holistic, multi-product system architectures, solution blueprints, and deployment recommendations for complex workloads on Google Cloud.

When should I use Google Cloud Solution Architecture?

Google Cloud Solution Architecture fits situations like: designing end-to-end cloud solutions; selecting and integrating Google Cloud services; generating architecture diagrams; conducting requirements discovery for new cloud workloads.

How do I install Google Cloud Solution Architecture in Claude Code?

Run `npx skills add google/skills --skill google-cloud-solution-architecture -a claude-code`. Or copy the skill folder (skills/cloud/google-cloud-solution-architecture in google/skills) into .claude/skills/google-cloud-solution-architecture in your project. Claude Code loads it when a task matches its description.

How do I install Google Cloud Solution Architecture in Codex?

Run `npx skills add google/skills --skill google-cloud-solution-architecture -a codex`. Or copy the skill folder (skills/cloud/google-cloud-solution-architecture in google/skills) into .agents/skills/google-cloud-solution-architecture in your project. Codex loads it when a task matches its description.

Can I use Google Cloud Solution Architecture in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add google/skills --skill google-cloud-solution-architecture -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/google-cloud-solution-architecture, .gemini/skills/google-cloud-solution-architecture, .github/skills/google-cloud-solution-architecture and .opencode/skills/google-cloud-solution-architecture in your project.

What does Google Cloud Solution Architecture need to run?

Going by SKILL.md and its folder, Google Cloud Solution Architecture needs the command-line tools its instructions call (terraform and gcloud).

Does Google Cloud Solution Architecture access the network?

SKILL.md names 2 domains. As links in the text: developerknowledge.googleapis.com and github.com. This is read from the text; nothing was executed.

Is Google Cloud Solution Architecture safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Google Cloud Solution Architecture use?

Google Cloud Solution Architecture is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Google Cloud Solution Architecture use?

About 3.5k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 29k tokens, read only when the agent opens those files.

What are the alternatives to Google Cloud Solution Architecture?

Skills that share tags, products or a category with Google Cloud Solution Architecture: Azure Diagrams (cmb211087/azure-diagrams-skill, 150 stars), GCP DrawIO Diagram Generator (a5c-ai/babysitter, 1.8k stars), Drawio Azure (sparklabx/drawio-ai-kit, 652 stars) and AWS DrawIO Diagram Generator (a5c-ai/babysitter, 1.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Google Cloud Solution Architecture?

google (a GitHub organization, an official publisher) maintains it in google/skills, which has 20,994 GitHub stars. The repository holds 145 skills in this directory. The repository was last updated on October 6, 2026.

Source: google/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.