cloud-databases-onboarding

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.

1. 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.

1. 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.

1. 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.

1. 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.

1. 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.

1. 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](../gcloud/SKILL.md)