design-deploy

name	design-deploy
description	Processes GCP infrastructure design and deployment workflows. 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. Don't use for non-GCP cloud providers, or general Terraform coding outside the ADC context.
license	Apache-2.0
metadata	{"version":"v1","publisher":"google","category":"CloudInfrastructure"}

Custom Infrastructure Design and Deployment Skill

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.

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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

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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:

     gcloud config set project <project_id>
     

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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:

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

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

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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:

     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:

     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:

   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:

     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.

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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:

     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:

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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.

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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:

     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 skill (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:

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

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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.