Official agent skill

GCP Application Design Center Deploy

by google in google/skills

Designs GCP infrastructure as local Terraform, validates and scans it against best practices, then imports it to Application Design Center for deployment and troubleshooting.

OfficialApache-2.0Auto-check passedDevOps & Cloud

Install GCP Application Design Center Deploy

skills CLI
$ npx skills add google/skills --skill application-design-center-design-deploy -a claude-code

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

GitHub CLI
$ gh skill install google/skills application-design-center-design-deploy --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/application-design-center-design-deploy .claude/skills/application-design-center-design-deploy && 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
application-design-center-design-deploy
GitHub stars
21k
Token cost
~4.4k tokens
SKILL.md length
1,815 words
Files
14 (incl. scripts, references)
Skills in repo
147
Repo updated
First seen
Licence
Apache-2.0

At a glance

Designs GCP infrastructure as local Terraform, validates and scans it against best practices, then imports it to Application Design Center for deployment and troubleshooting.

  • Works in 6 steps: Local Infrastructure Design & Validation → Shifted-Left Best Practices Assessment &… → Import IaC to Application Design Center → …
  • Designing GCP infrastructure with Terraform from a set of requirements
  • SKILL.md covers Overview, Index, Pre-requisites: Setup &… and Phase 1: Local Infrastructure…, plus 6 more sections
  • Runs Python scripts from its folder; calls terraform and gcloud

What it does

The skill lays out a production-minded workflow for the whole infrastructure lifecycle on Google Cloud. It replaces an automated, opaque design tool with a loop the agent controls: modular Terraform written locally, validated with local CLI tools, then given a best-practices plan scan before anything is synchronized with the Application Design Center registry. The local Terraform configuration stays the source of truth, and the agent takes the stance of a principal cloud architect.

Setup comes first: the agent confirms the target project ID and location, defaults to `us-central1` when none is given, and checks that `gcloud` has the right project active. Six phases follow: local design and validation, the plan scan with iterative remediation, import into Application Design Center, deployment and monitoring, troubleshooting failures, and verification with end-to-end tests. The folder has Python scripts to list and fetch Terraform templates, with tests, plus reference guides for planning, generating, validating, remediation and error analysis. It applies only to GCP and to Terraform used within Application Design Center.

When your agent uses it

  • Designing GCP infrastructure with Terraform from a set of requirements
  • Validating local HCL and scanning the plan before deploying
  • Importing a Terraform template into Application Design Center and deploying it
  • Troubleshooting a failed Application Design Center deployment

Example prompts

  • “Design a Cloud Run service with a Cloud SQL database in Terraform and validate it locally.”
  • “Run the best-practices plan scan on my Terraform and fix what it flags.”
  • “Import this template into Application Design Center and deploy it to europe-west1.”
  • “My Application Design Center deployment failed, work out why.”

Requirements

  • A GCP project ID and location
  • gcloud CLI with an active project
  • Terraform for local validation

Workflow steps

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

  1. Local Infrastructure Design & Validation
  2. Shifted-Left Best Practices Assessment & Iterative Remediation
  3. Import IaC to Application Design Center
  4. Application Deployment & Monitoring
  5. Troubleshoot Deployment Failures
  6. Verification & E2E Testing

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

    Ships 4 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • terraform
    • gcloud

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

  • Network

    No URLs in SKILL.md. Its commands use gcloud, which can reach the network depending on how they are called.

    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

GCP Application Design Center Deploy loads about 4.4k tokens when it runs, and up to ~18k if it reads all its reference files. Until then it costs about 125 tokens; SKILL.md has 1,815 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~125
When it runs · the whole SKILL.md, loaded when a task matches
~4.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~18k

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); the scripts in this folder are not scanned.

SKILL.md

The full file from google/skills at commit 7d97937, republished under its Apache-2.0 licence (© google). 1,815 words, ~4,405 tokens.

Download SKILL.mdSave it as .claude/skills/application-design-center-design-deploy/SKILL.md (or your agent's skills folder). This skill also uses 13 other files; get the full folder from GitHub.
name
application-design-center-design-deploy
description
Processes GCP infrastructure design and deployment workflows within Application Design Center (ADC). Use when: - Designing GCP infrastructure with Terraform. - Validating local HCL. - Performing best-practice plan scans. - Importing templates to Application Design Center (ADC). - Deploying templates. - Troubleshooting deployment failures. Boundaries: - Only use for GCP-specific cloud infrastructure. - Only use for Terraform coding within the ADC context.
license
Apache-2.0
metadata.version
1.0.0
metadata.publisher
google
metadata.category
CloudInfrastructure

Designing and Deploying GCP Infrastructure with Application Design Center

Overview

This skill provides a prescriptive, production-grade workflow for the entire infrastructure lifecycle on Google Cloud Platform (GCP). It replaces the automated, opaque-box GAD design_infra tool with an agent-controlled design and validation loop utilizing modular Terraform and local CLI validation, followed by a shifted-left best practices plan scan prior to synchronization with the Application Design Center (ADC) registry for deployment and lifecycle management.

Always maintain the persona of a Principal Cloud Architect. Keep the local Terraform configuration as the source of truth, and ensure the design is fully compliant with best practices before importing it into the cloud registry.


Index

  1. Pre-requisites: Setup & Confirmation
  2. Phase 1: Local Infrastructure Design & Validation
  3. Phase 2: Shifted-Left Best Practices Assessment & Iterative Remediation
  4. Phase 3: Import IaC to Application Design Center
  5. Phase 4: Application Deployment & Monitoring
  6. Phase 5: Troubleshoot Deployment Failures
  7. Phase 6: Verification & E2E Testing

Pre-requisites: Setup & Confirmation

Before executing Phase 1, you must perform the following setup steps:

  1. Confirm Target Project & Location:

    • Explicitly ask the user to confirm the target GCP project ID and location (region).

    • If the user does not specify a location, use us-central1 as the default.

    • Verify that your local environment has the active project set:

      bash
      gcloud config set project <project_id>

Phase 1: Local Infrastructure Design & Validation

Goal: Transform user requirements and codebase characteristics into a 100% validated, secure, and compile-ready Terraform configuration locally.

  1. Invoke the design Skill: Call and execute the design skill (defined in design) for the user's prompt.

    • The design skill will autonomously perform the Codebase Analysis, query the catalog registry, planning, HCL generation, and local CLI validation loop (terraform init, validate, plan) in a dedicated scratch directory.
  2. Locate Validated HCL: Identify the scratch directory where the design skill saved the validated, compile-ready Terraform files (e.g., scratch/tf_validate_<session_id>/).

  3. Verify Handover (MANDATORY): Ensure that the local validation loop in the design skill completed successfully with a clean plan before proceeding. Meticulously inspect the HCL to verify:

    • Secret-Safe Policy: Confirm that no plaintext credentials, passwords, or hardcoded secrets are written in terraform.tfvars or HCL resource blocks. All sensitive inputs must be wired through GCP Secret Manager.
    • State Isolation Policy: Confirm that there is no remote backend block (e.g., backend "gcs" {}) in the HCL files. State must remain local in the scratch folder during validation, allowing ADC to handle the remote state registry upon import.
    • Remediation: If any violations are found, correct them in the HCL, re-run local validation, and verify again. Do not proceed with unvalidated or insecure code.
  4. Export Terraform Plan to JSON (MANDATORY): In the scratch directory, run the following commands to generate a binary plan and convert it into a clean JSON representation:

    bash
    terraform plan -out=tfplan && terraform show -json tfplan > tfplan.json

    Verify that the tfplan.json file is successfully written in your scratch directory.


Phase 2: Shifted-Left Best Practices Assessment & Iterative Remediation

Goal: Validate the local plan's alignment with security, cost, and reliability benchmarks BEFORE importing it into the cloud registry, using the native ADC plan assessment API.

  1. Discover Space ID (MANDATORY): Before running the assessment or creating templates, you must dynamically discover the active ADC Space ID in your target location:

    • List Spaces: Run the command:

      bash
      gcloud design-center spaces list --project=<project_id> --location=<location>
    • Select Space: Parse the output to identify the active space (e.g., test-deploy or googlespace). If multiple spaces exist, ask the user to confirm. If no space exists, ask the user or create one:

      bash
      gcloud design-center spaces create <space_id> --project=<project_id> --location=<location>
  2. Execute Plan Assessment via gcloud: Run the plan-based assessment using the discovered Space ID and your exported tfplan.json file. Execute the command directly in your terminal:

    bash
    gcloud design-center spaces generate-terraform-assessment-report <space_id> \
        --location=<location> \
        --project=<project_id> \
        --terraform-plan="<scratch_directory_path>/tfplan.json" \
        --format=json
  3. Analyze Findings: Present all findings to the user in a clean tabular format, detailing specific violations, resource scopes, and associated severity levels.

  4. Local Remediation Loop:

    • Do not attempt to import or commit insecure code.

    • Edit your local HCL files in the scratch directory to fix the reported violations (e.g., adding encryption keys, enabling OS Login, or restricting IAM scopes).

    • Re-run Phase 1 local validation and plan export:

      bash
      terraform validate && terraform plan -out=tfplan && terraform show -json tfplan > tfplan.json
    • Re-run the plan assessment command shown in step 2.

  5. Exit Criteria:

    • All high/critical findings resolved, or acceptable trade-offs documented.
    • Maximum of three (3) iterative attempts reached. Once clean or acceptable, proceed to Phase 3.

Phase 3: Import IaC to Application Design Center

Goal: Synchronize the fully validated and best-practice-compliant local HCL configuration with the ADC cloud registry to establish the deployable template resource.

  1. Verify or Create the Application Template (MANDATORY): Before importing the HCL, you must ensure the parent Application Template resource exists in the discovered ADC space.

    • Check Existence: Run gcloud design-center spaces application-templates describe <template_id> --space=<space_id> --project=<project_id> --location=<location> to check if the template exists.

    • Create if Missing: If the describe command returns a NOT_FOUND error, create the template resource first by running:

      bash
      gcloud design-center spaces application-templates create <template_id> --space=<space_id> --project=<project_id> --location=<location> --display-name="<Name>" --description="<Description>"
  2. Strict HCL Parser Constraints (CRITICAL): Before calling the import operation, ensure your local HCL complies with the ADC registry's strict ingestion rules:

    • Pure Module Policy (No Resource Blocks): The ADC parser strictly prohibits any resource blocks inside the imported HCL. Only module, variable, output, and provider blocks are allowed. If a resource is required (e.g. Private Service Access peering) but no standalone module is registered for it in the catalog, you MUST check if it is supported as a built-in configuration option inside an existing registered module (e.g. setting private_service_access_config inside module "vpc").
    • Strict String Typing: The ADC parser does not perform implicit type coercion from boolean to string. For example, subnet private access must be declared as a literal string: subnet_private_access = "true", NOT as a boolean true.
    • No Terraform Block: The parser strictly prohibits the terraform {} version constraint block. Omit it entirely from providers.tf or main.tf.
  3. Import to ADC Template: Once the template resource is confirmed to exist and the HCL is validated against the above constraints, invoke the hosted application_design_center:manage_application_template MCP tool with the APPLICATION_TEMPLATE_OPERATION_IMPORT_IAC operation:

    • Arguments:

      • project: The target project ID.

      • location: The GCP deployment region (e.g., us-central1).

      • spaceId: The discovered ADC space ID.

      • applicationTemplateId: A unique name for your application template.

      • operation: APPLICATION_TEMPLATE_OPERATION_IMPORT_IAC

      • iacModule: A structured object containing the files list:

        json
        {
          "files": [
            { "name": "main.tf", "content": "<content of main.tf>" },
            { "name": "variables.tf", "content": "<content of variables.tf>" },
            { "name": "terraform.tfvars", "content": "<content of terraform.tfvars>" }
          ]
        }
    • Resilience & Retries (MANDATORY):

      • If the IMPORT_IAC call fails due to a transient error (e.g., 502 Bad Gateway, 504 Gateway Timeout, or 429 Rate Limit), do not immediately retry.
      • Use exponential backoff with jitter (e.g., waiting 2s, 4s, 8s plus a random fraction of a second).
      • Verify Revision before Retry: If a timeout occurred, first call gcloud alpha design-center spaces application-templates describe to check if the import actually succeeded in the background. Only retry if the template was not updated.
  4. Capture Template URI: Upon success, this establishes the template resource in your space. Construct the applicationTemplateUri using the pattern: projects/{project}/locations/{location}/spaces/{spaceId}/applicationTemplates/{applicationTemplateId}


Show full SKILL.md (633 more words)Show less

Phase 4: Application Deployment & Monitoring

Goal: Deploy the validated, best-practice-compliant application template to the GCP environment.

  1. Deploy Application: Invoke the hosted application_design_center:manage_application MCP tool with the APPLICATION_OPERATION_DEPLOY operation:
    • Arguments:
      • project: Target project ID.
      • location: Target deployment location.
      • spaceId: Target space ID.
      • applicationId: A unique ID for the deployed application instance.
      • applicationTemplateUri: The URI established in Phase 3.
      • serviceAccount: The deployment service account.
    • Resilience & Retries (MANDATORY):
      • If the DEPLOY operation fails with transient network or gateway errors (e.g., 502, 504), apply exponential backoff with jitter before retrying.
      • If the deployment LRO times out or fails with a state conflict, verify the application status using gcloud design-center spaces applications describe to confirm its status before retrying the deploy call, avoiding concurrent conflicting deployments.
  2. Active LRO Monitoring:
    • The tool returns a Long-Running Operation (LRO). Inform the user that the deployment has started.
    • Do not sleep during deployment status polling. Poll the LRO actively every 30–60 seconds until done: true using the command gcloud design-center operations describe <operation_name>.
  3. Handle Results:
    • Success: If done is true and there is no error field, proceed to Phase 6.
    • Failure: If an error field is present, analyze the error type and proceed to Phase 5.

Phase 5: Troubleshoot Deployment Failures

Goal: Diagnose and remediate deployment failures iteratively using the specialized troubleshooting skill and established cloud resolution patterns.

  1. Iterative Cloud Resolution Patterns (CRITICAL): If the deployment fails with a REVISION_FAILED or TERRAFORM error, check for these common resource conflicts:

    • Service Account 409 Conflict (alreadyExists): If the deployment fails because a service account generated by the module (e.g. frontend-service-us-central-sa) already exists in the project, remediate the local HCL by disabling service account creation and referencing the existing one:

      hcl
      create_service_account = false
      service_account        = "<existing_service_account_email>"
    • Container Image 404 NotFound: If the deployment fails because a container image is not found, confirm that the image exists in your registry. For testing or hello-world deployments, leverage the official public Google hello-world image: us-docker.pkg.dev/cloudrun/container/hello

  2. Delegate to the Troubleshooting Skill: If a deployment failure occurs and does not match the above patterns, invoke and execute the specialized infra-deployment-debugging guide (located in infra-deployment-debugging).

  3. Select the Troubleshooting Context:

    • For Local Validation Errors (Phase 1/2): Follow Case B: Raw Terraform Deployment instructions in the troubleshooting skill to isolate syntax, compilation, and plan-time validation errors.
    • For Cloud Deployment Failures (Phase 4): Follow Case A: ADC Application Deployment instructions in the troubleshooting skill to analyze LRO errors, retrieve service logs, and diagnose cloud environment issues.
  4. Apply Local-First Remediation:

    • Follow the troubleshooting skill's remediation guides to formulate a fix.

    • MANDATORY: Apply the fix directly to your local HCL files in the scratch directory, re-run local validation, re-import the HCL, and trigger a new deployment.

    • Re-run Phase 1 local validation and plan export:

      bash
      terraform validate && terraform plan -out=tfplan && terraform show -json tfplan > tfplan.json
    • Re-run the plan assessment (Phase 2) to ensure no new violations are introduced.

    • Re-import the corrected HCL to ADC using APPLICATION_TEMPLATE_OPERATION_IMPORT_IAC.

    • Trigger a new deployment using APPLICATION_OPERATION_DEPLOY.

  5. Iteration Threshold: Repeat the troubleshooting, validation, import, and redeployment cycle up to five (5) times. If it still fails, report the full history and diagnostics to the user.


Phase 6: Verification & E2E Testing

Goal: Confirm that the deployed services are healthy and fully functional.

  1. Retrieve Deployed Resources: Invoke the hosted application_design_center:manage_application MCP tool with the APPLICATION_OPERATION_GET operation to retrieve the resource details, public endpoints, and output parameters.
  2. Health Check: Verify that all services are using the correct container image URLs and that their runtime status is healthy.
  3. E2E Validation: Conduct a simple demo test (e.g., checking public HTTP endpoints or triggering a dry-run transaction) to ensure E2E functionality. Present the results and public URLs to the user to conclude the task.

Reporting Issues

Report bugs or improvements for this skill at Google Skills Issues.

© 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 13 other files (scripts, references) in skills/cloud/application-design-center-design-deploy of google/skills.

  • SKILL.md
  • references/adc_application_troubleshooting.md
  • references/design_guide.md
  • references/error_analysis_guide.md
  • references/generator_instructions.md
  • references/planner_instructions.md
  • references/raw_terraform_troubleshooting.md
  • references/remediation_guide.md
  • references/terraform_validator_instructions.md
  • references/troubleshooting_guide.md
  • scripts/fetch_terraform_template.py
  • scripts/fetch_terraform_template_test.py
  • scripts/list_terraform_templates.py
  • scripts/list_terraform_templates_test.py

Open the folder on GitHubat commit 7d97937

Compare with similar skills

GCP Application Design Center Deploy 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.

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Terraform Module Librarywshobson/agents40k11 repos~1.3kAutomated safety check: PassMIT
DeployingGoogleCloudPlatform/race-condition234—~3kAutomated safety check: PassCustom licence
Cloud Architectdavila7/claude-code-templates32k8 repos~1.9kAutomated safety check: PassMIT

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Categories

Questions about GCP Application Design Center Deploy

What does GCP Application Design Center Deploy do?

Designs GCP infrastructure as local Terraform, validates and scans it against best practices, then imports it to Application Design Center for deployment and troubleshooting. The skill lays out a production-minded workflow for the whole infrastructure lifecycle on Google Cloud. It replaces an automated, opaque design tool with a loop the agent controls: modular Terraform written locally, validated with local CLI tools, then given a best-practices plan scan before anything is synchronized with the Application Design Center registry.

When should I use GCP Application Design Center Deploy?

GCP Application Design Center Deploy fits situations like: designing GCP infrastructure with Terraform from a set of requirements; validating local HCL and scanning the plan before deploying; importing a Terraform template into Application Design Center and deploying it; troubleshooting a failed Application Design Center deployment.

How do I install GCP Application Design Center Deploy in Claude Code?

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

How do I install GCP Application Design Center Deploy in Codex?

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

Can I use GCP Application Design Center Deploy 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 application-design-center-design-deploy -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/application-design-center-design-deploy, .gemini/skills/application-design-center-design-deploy, .github/skills/application-design-center-design-deploy and .opencode/skills/application-design-center-design-deploy in your project.

What does GCP Application Design Center Deploy need to run?

Going by SKILL.md and its folder, GCP Application Design Center Deploy needs Python for the scripts in its folder and the command-line tools its instructions call (terraform and gcloud). Our summary lists: A GCP project ID and location; gcloud CLI with an active project; Terraform for local validation.

Does GCP Application Design Center Deploy access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is GCP Application Design Center Deploy 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does GCP Application Design Center Deploy use?

GCP Application Design Center Deploy is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does GCP Application Design Center Deploy use?

About 4.4k tokens (SKILL.md is roughly 18k 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 13k tokens, read only when the agent opens those files.

What are the alternatives to GCP Application Design Center Deploy?

Skills that share tags, products or a category with GCP Application Design Center Deploy: Cloud Infrastructure (aiskillstore/marketplace, 430 stars), Senior DevOps Toolkit (maslennikov-ig/claude-code-orchestrator-kit, 260 stars), Terraform Module Library (wshobson/agents, 40k stars) and Deploying (GoogleCloudPlatform/race-condition, 234 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains GCP Application Design Center Deploy?

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.