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Official agent skill

Google Cloud Solution Build Deploy Agents

by google in google/skills

Designs, builds, and deploys AI agents or multi-agent systems on Google Cloud.

OfficialApache-2.0Auto-check passedAgent Workflows

Install Google Cloud Solution Build Deploy Agents

skills CLI
$ npx skills add google/skills --skill google-cloud-solution-build-deploy-agents -a claude-code

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

GitHub CLI
$ gh skill install google/skills google-cloud-solution-build-deploy-agents --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-build-deploy-agents .claude/skills/google-cloud-solution-build-deploy-agents && 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-build-deploy-agents
GitHub stars
21k
Token cost
~3.5k tokens
SKILL.md length
1,592 words
Files
7 (incl. references, assets)
Skills in repo
147
Repo updated
First seen
Licence
Apache-2.0

At a glance

Designs, builds, and deploys AI agents or multi-agent systems on Google Cloud.

  • Works in 4 steps: Requirements discovery and analysis → Solution design → Implementation plan → …
  • Implementing agentic systems on Google Cloud
  • SKILL.md covers Workflow and References & Supporting Links
  • Calls terraform

What it does

Google Cloud Solution Build Deploy Agents is an agent skill from google/skills, published by the product's own GitHub organization. Designs, builds, and deploys AI agents or multi-agent systems on Google Cloud. Provides an interactive workflow to gather requirements, recommend a tailored architecture, and generate deployment instructions. Use when designing or implementing agentic systems on Google Cloud. Don't use for general Google Cloud solution architecture (use google-cloud-solution-architecture instead) or for narrow tasks targeting a single product without agent context.

Its SKILL.md is about 3.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including reference files and assets (for example `assets/implementation-template.md`, `assets/solution-template.md` and `assets/validation-template.md`).

It sits in Agent Workflows, covering Software architecture, Deployment and Codebase knowledge for agents. 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

  • Implementing agentic systems on Google Cloud
  • General Google Cloud solution architecture (use google-cloud-solution-architecture instead)
  • For narrow tasks targeting a single product without agent context

Example prompts

  • “/google-cloud-solution-build-deploy-agents”

Workflow steps

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

  1. Requirements discovery and analysis
  2. Solution design
  3. Implementation plan
  4. Solution validation

What it can do on your machine

Read from SKILL.md and the folder at commit 7d97937. 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

    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):

    • 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 Build Deploy Agents loads about 3.5k tokens when it runs, and up to ~8.4k if it reads all its reference files. Until then it costs about 124 tokens; SKILL.md has 1,592 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~124
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
~8.4k

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 7d97937, republished under its Apache-2.0 licence (© google). 1,592 words, ~3,457 tokens.

Download SKILL.mdSave it as .claude/skills/google-cloud-solution-build-deploy-agents/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
google-cloud-solution-build-deploy-agents
description
Designs, builds, and deploys AI agents or multi-agent systems on Google Cloud. Provides an interactive workflow to gather requirements, recommend a tailored architecture, and generate deployment instructions. Use when designing or implementing agentic systems on Google Cloud. Don't use for general Google Cloud solution architecture (use google-cloud-solution-architecture instead) or for narrow tasks targeting a single product without agent context.
metadata.version
1.0.0
metadata.category
MultiProductSolutions

Build and deploy AI agents on Google Cloud

This skill guides agents through the workflow of designing and implementing a tailored multi-product solution in the cloud for a given workload, use case, or requirement.

Workflow

The solution design and implementation workflow is divided into the following phases:

  • Phase 1: Requirements discovery and analysis: Analyze the workload's requirements, constraints, dependencies, and current state.
  • Phase 2: Solution design: Build a technology stack, architecture, and deployment configuration for the workload based on Google Cloud design best practices and recommendations.
  • Phase 3: Implementation plan: Generate automation and instructions to deploy the solution.
  • Phase 4: Solution validation: Validate that the deployment meets the requirements of the workload.

Copy this checklist into your active task/plan artifact to track progress across the four phases:

  • Phase 1: Requirements discovery and analysis completed & confirmed.
  • Phase 2: Solution architecture generated & approved.
  • Phase 3: Implementation plan generated & approved.
  • Phase 4: Solution validation generated & approved.
Phase 1: Requirements discovery and analysis

  1. Discover requirements: Gather and understand the functional and non-functional requirements, business goals, and current state (if any) of the workload, including its architecture, dependencies, and constraints.

    Important: First, check whether the user's initial prompt has already answered the following questions or whether the prompt explicitly asks you to propose a solution architecture/diagram from a given set of parameters.

    • If the user's prompt provides sufficient requirements and it explicitly requests an architecture proposal or diagram, then skip asking the questions below, and instead proceed to the step Recommend agent design pattern.

    • If the user's prompt doesn't provide sufficient requirements, then complete these steps to gather missing information:

      1. Ask the user to describe the functional requirements of their workload: business processes, activities, and use cases.

      2. Ask the user to describe the non-functional requirements (security, privacy, compliance, reliability, disaster recovery, cost, operations, performance, and sustainability) of their workloads.

      3. Ask the user what existing systems, knowledge bases, product documentation, or other documentation the AI agents need to access for grounded guidance.

      4. Ask the user to describe dependencies, if any, on other workloads, products, or tools.

      5. Review the input that the user has provided so far, and check whether there are any ambiguities or contradictions in the input.

        If you identify any ambiguities or contradictions in the requirements that the user has provided, then do the following for each ambiguity or contradiction that you identify:

        • Describe the ambiguity or contradiction.
        • Ask the user how they wish to resolve the ambiguity or contradiction.
          • 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 according to the preceding guidance, you must NOT recommend or generate any architecture design, technical decomposition, or Google Cloud product recommendations.

  2. Recommend agent design pattern: Evaluate the complexity, workflow, latency, and cost requirements of the workload to recommend an agent design pattern:

    • Single-agent system: Recommend for simpler tasks, acting as an effective starting point to refine core logic and tools.
    • Multi-agent system: Recommend for complex problems requiring multiple specialized agents to collaborate on a workflow.

  3. Identify components: Based on the requirements analysis, generate a technical decomposition of the workload. The technical decomposition must identify the logical components of the workloads and their relationships. Also identify any cross-cloud components, hybrid components, or on-premises components that the solution needs to integrate with.

  4. Ask for confirmation: Ask the user to confirm whether the recommended design pattern and technical decomposition match their workload requirements.

  5. Iterate: If the user requests changes, generate an updated technical decomposition, and ask the user to confirm the changes. Continue iterating until the user confirms the technical decomposition. Proceed to the next phase only after the user provides confirmation of the technical decomposition.

Phase 2: Solution design

  1. Retrieve relevant Google Cloud guidance from references/related-guidance.md.

    Important: Use the content that you retrieved from references/related-guidance.md to ground the guidance that you generate in the remaining steps of this phase.

  2. Map components to Google Cloud products: For each component in the confirmed technical decomposition, identify the appropriate Google Cloud products and features by consulting product-mappings.md for detailed recommendations, trade-offs, and alternatives across networking, frontends, agent/model runtimes, memory stores, and tools.

  3. Create architecture diagram: Create an architecture diagram in Mermaid format: https://github.com/mermaid-js/mermaid. The diagram should show the components, their relationships, and data/control flows.

  4. Generate design recommendations: Generate design guidance based on the following Google Cloud best practices and recommendations. Use the information in references/related-guidance.md, with an emphasis on the guidance in references/design-principles.md.

  5. Draft solution architecture: Compile the requirements, technical decomposition, product mapping, architecture diagram, and design recommendations into a single Markdown file adhering to the format in solution-template.md. Save this document in the workspace as solution-architecture.md.

  6. Request review: Present the generated solution architecture (including the complete fenced mermaid code block for the diagram) directly to the user in your response, and explicitly request their feedback or approval. When you present the architecture, ask the user to provide approval for you to proceed with an implementation plan.

  7. Iterate: If the user requests changes, generate an updated solution architecture and repeat the steps from "Map components to Google Cloud products" through "Request review" until the user approves the solution architecture.

Show full SKILL.md (689 more words)Show less
Phase 3: Implementation plan

  1. Retrieve relevant implementation resources:

    Important: Use the resources in references/related-guidance.md as the technical foundation for the Infrastructure as Code (IaC) and the deployment instructions that you generate in the remaining steps of this phase.

  2. Identify deployment prerequisites: Document prerequisites for the deployment, including the following:

    • Projects and billing associations
    • Required Google Cloud APIs
    • Required IAM permissions
    • Any other prerequisites

  3. Generate Infrastructure as Code (IaC): Generate code (e.g., Terraform) and deployment scripts to automate the provisioning of the proposed Google Cloud resources.

    • Where appropriate, alongside or instead of raw infrastructure scripts, instruct the user to use Agents CLI commands (agents-cli scaffold create or agents-cli scaffold enhance) to set up or enhance the project structure, deployment configuration, and CI/CD pipelines.

  4. Write deployment instructions: Draft sequential, step-by-step deployment instructions to execute the IaC and initialize the workload components. Compile the deployment prerequisites, IaC, and deployment instructions into a single Markdown file adhering to the format in implementation-template.md. Save this document in the workspace as implementation-instructions.md.

    • The instructions MUST provide the exact ADK code to define a stateful agent node that takes a prompt, calls a model, and returns a tool execution request.
    • The instructions MUST demonstrate how to register tools like database readers by using Model Context Protocol (MCP) standards.
    • If deploying the agent to Cloud Run, the instructions MUST show how to configure Cloud Run to scale to zero when the agent is idle, reducing runtime costs.
    • The instructions MUST recommend using encrypted environment variables to store model parameters or private API credentials. Encryption helps to prevent the exposure of sensitive credentials in plain-text container log streams.
    • Where appropriate, the instructions MUST specify using the Agents CLI agents-cli deploy command (alongside or instead of raw infrastructure/deployment scripts) to run the deployment.

  5. Request review: Present the generated deployment instructions to the user and explicitly request their feedback and confirmation.

  6. Iterate: If the user requests changes, generate an updated implementation plan and repeat the steps from "Generate Infrastructure as Code (IaC)" through "Request review" until the user approves the implementation plan.

Phase 4: Solution validation

  1. Retrieve relevant verification resources:

    Important: Use the resources in references/related-guidance.md and their verification patterns as the starting point for the validation checks and verification scripts that you generate in the remaining steps of this phase.

  2. Define validation checks: Outline validation steps to verify that the deployed infrastructure meets the workload's requirements:

    • Deployment dry-run: Commands like terraform plan to preview changes. Include instructions to run agent deployment in dry-run mode (e.g., using agents-cli deploy --dry-run or -n) to preview steps and Terraform executions before pushing to production.
    • Local testing and quality verification: Recommend using the Agents CLI to run and test agent logic locally (agents-cli run) and conduct systematic evaluations (agents-cli eval run) to verify agent quality and performance before deploying.
    • Connectivity and routing: Verification of network paths, load balancer routing, and service endpoints.
    • Security policies: Verification of restricted access, firewall rules, and IAM enforcement.

  3. Generate verification scripts: Draft lightweight scripts or command-line instructions (e.g. using curl, gcloud, or agents-cli) that the user can run to perform these validation checks.

    • The validation plan MUST include instructions using the Agents CLI for local runs, evaluations, and post-deployment validation checks (e.g., agents-cli run --url <service-url> to test the deployed service endpoint).

  4. Compile validation plan: Document the validation steps, verification scripts, and expected outcomes in a single Markdown file adhering to the format in validation-template.md. Save this document in the workspace as validation-plan.md.

  5. Request review: Present the validation plan to the user and explicitly request their feedback or approval on the validation plan.

  6. Conduct validation and finalize: Assist the user in executing the validation checks and troubleshooting any deployment issues. After the solution is validated successfully, request final approval from the user.

  7. Iterate: If the user requests changes, generate an updated validation plan and repeat the steps from "Define validation checks" through "Request review" until the user approves the validation plan.

  • For the complete list of Google Cloud architectural documentation, product manuals, development kits, and checklists used by this skill, see related-guidance.md.

© 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 6 other files (references, assets) in skills/cloud/google-cloud-solution-build-deploy-agents of google/skills.

  • SKILL.md
  • assets/implementation-template.md
  • assets/solution-template.md
  • assets/validation-template.md
  • references/design-principles.md
  • references/product-mappings.md
  • references/related-guidance.md

Open the folder on GitHubat commit 7d97937

Compare with similar skills

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Project Context SetupMathews-Tom/armory328—~1.2kAutomated safety check: PassMIT
Adk Agent Builderjeremylongshore/tons-of-skills-marketplace2.8k—~960Automated safety check: PassMIT
Large Codebase Knowledge Base BuilderTencent/teamai-cli5.1k—~4.4kAutomated safety check: PassCustom licence

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Works with

Categories

Questions about Google Cloud Solution Build Deploy Agents

What does Google Cloud Solution Build Deploy Agents do?

Designs, builds, and deploys AI agents or multi-agent systems on Google Cloud. Google Cloud Solution Build Deploy Agents is an agent skill from google/skills, published by the product's own GitHub organization. Designs, builds, and deploys AI agents or multi-agent systems on Google Cloud.

When should I use Google Cloud Solution Build Deploy Agents?

Google Cloud Solution Build Deploy Agents fits situations like: implementing agentic systems on Google Cloud; general Google Cloud solution architecture (use google-cloud-solution-architecture instead); for narrow tasks targeting a single product without agent context.

How do I install Google Cloud Solution Build Deploy Agents in Claude Code?

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

How do I install Google Cloud Solution Build Deploy Agents in Codex?

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

Can I use Google Cloud Solution Build Deploy Agents 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-build-deploy-agents -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-build-deploy-agents, .gemini/skills/google-cloud-solution-build-deploy-agents, .github/skills/google-cloud-solution-build-deploy-agents and .opencode/skills/google-cloud-solution-build-deploy-agents in your project.

What does Google Cloud Solution Build Deploy Agents need to run?

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

Does Google Cloud Solution Build Deploy Agents access the network?

SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.

Is Google Cloud Solution Build Deploy Agents 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 Build Deploy Agents use?

Google Cloud Solution Build Deploy Agents 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 Build Deploy Agents 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 5k tokens, read only when the agent opens those files.

What are the alternatives to Google Cloud Solution Build Deploy Agents?

Skills that share tags, products or a category with Google Cloud Solution Build Deploy Agents: Project Structure Map (steipete/agent-scripts, 7.3k stars), Cloc (managedcode/dotnet-skills, 486 stars), Project Context Setup (Mathews-Tom/armory, 328 stars) and Adk Agent Builder (jeremylongshore/tons-of-skills-marketplace, 2.8k 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 Build Deploy Agents?

google (a GitHub organization, an official publisher) maintains it in google/skills, which has 21,032 GitHub stars. The repository holds 147 skills in this directory. The repository was last updated on October 8, 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.