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name: cloud-databases-onboarding metadata: version: "1.0.0" category: Databases description: >- Guides users through discovering their database requirements, recommends a Google Cloud database based on a recommendation matrix, and assists in database creation. Use when a user asks 'What database service should I use?', 'Help me pick a database', or when a user wants to create a new database on Google Cloud. Don't use for general Google Cloud maintenance, managing existing databases, or database migrations.

Google Cloud Database Onboarding Skill

This skill provides domain instructions, decision matrices, and Infrastructure-as-Code workflows to guide users through discovering their exact database requirements, selecting an optimal Google Cloud database service, and drafting starter resource provisioning code for user review.

Validation & Progressive Disclosure

A validation script is provided to verify the skill's reference files and formatting:

python3 scripts/database_onboarding_skill.py --verify

• Reading / Progressive Disclosure: When interacting with a user during a conversation, load reference files progressively. Follow the Just-in-Time (JiT) loading instructions outlined in the phases below.

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Workflow & Just-in-Time (JiT) Instructions

This workflow operates in three distinct sequential phases. Evaluate the active conversation history to determine the current phase and follow the corresponding instructions:

Phase 1: Requirement Discovery & Information Gathering

When a user asks "What database should I use?" or requires guidance on Google Cloud database selection, you must initiate the Discovery phase.

1. Load Discovery Instructions (JiT): Read the complete contents of references/onboarding_prompts.md using view_file.
2. Execute Discovery: Follow the detailed Phase 1 instructions in onboarding_prompts.md to gather core requirements (data model, workload, scale, and migration context) using user-friendly phrasing and enforcing constraints (such as the 90% confidence rule) before proposing any recommendation.

Phase 2: Recommendation Analysis & Matrix Consultation

Once you have gathered sufficient explicit discovery context, you must determine the optimal Google Cloud database recommendation.

1. Consult Matrix & Formulate Recommendation (JiT): Follow the Phase 2 instructions in references/onboarding_prompts.md. This involves distilling requirements, calling the database selection tool (or consulting references/recommendation_matrix.txt directly if the tool is unavailable), and formulating a single recommendation.
2. Deliver Recommendation: Deliver the recommendation to the user, mapping destination codes to plain English, explaining the reasoning, and offering to help with provisioning as detailed in onboarding_prompts.md.

Phase 3: Implementation & Provisioning (Plan-Validate-Execute Pattern)

When the user accepts the recommendation and requests to provision or modify cloud resources, follow the Phase 3 instructions in references/onboarding_prompts.md using a strict Plan-Validate-Execute pattern. Limit your actions to creating and validating draft artifacts for user review.

1. Analyze the Workspace: Scan the user's workspace/open files/related directories with database resources scripts.

2. Obtain User Confirmation: If the target infrastructure files are not clear, ask the user explicitly to confirm the file paths or target directory before modifying anything.

3. Draft Infrastructure Plan (Plan): Create or edit the necessary Terraform configuration files or any other relevant scripts necessary to provision the resources. When creating or editing Terraform files or any other database resource provisioning script, you MUST:

   • Add a stamped header comment at the top of every generated Terraform file/ shell script or any other resource provisioning script. (e.g., # Generated with cloud onboarding skills selector @date, replacing @date with the current date/timestamp).
   • Add a custom default tag like resource_generated_by = "cloud db onboarding skill" under the default_tags block or as a resource label/tag.
   • gcloud CLI Generation: When drafting gcloud CLI commands or shell scripts, you MUST follow the instructions in the gcloud skill (../gcloud/SKILL.md). Specifically:
     • Always use gcloud beta command group for database provisioning (e.g., gcloud beta <group> <resource> create).
     • Validate leaf-level syntax using gcloud help <leaf_command> prior to proposing commands.
     • Append explicit --project=<PROJECT_ID> and explicit location flags (--region, --zone, or --location).
     • Use --dry-run or --validate-only preview flags where supported.
     • Include custom label/tag flags (e.g. --labels=resource_generated_by=cloud_db_onboarding_skill) on generated gcloud provisioning commands.
     • Do NOT include --quiet (-q): Provisioning commands are drafted for interactive human user review and execution, so do NOT include non-interactive --quiet or -q flags.
     • No Live Write Execution: The skill MUST ONLY draft provisioning commands or code for user review and MUST NOT execute mutating/write infrastructure operations directly.

4. Validate Infrastructure Code (Validate): Before finalizing, you must validate the drafted infrastructure code to verify syntax and configuration correctness. Why this matters: Validating Terraform code ensures that configuration blocks, IAM bindings, and instance sizing are syntax-error-free and strictly enforceable before code review.

5. Create Pull Request (Execute): Once validation succeeds with zero errors, automatically create a Pull request containing the validated Terraform/shell/scripts updates for user review. Leave live infrastructure changes (terraform apply or gcloud commands) to human review or automated CI/CD pipelines.

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Supporting Resources & Documentation

• Google Cloud Databases Overview
• gcloud CLI Skill