visa-data-modeler

Model prospective data schemas with entities, relationships, and ERD structures.

Updated May 4, 2026
One-click install
npx skills add https://github.com/Adgmed2018/visa --skill visa-data-modeler
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: visa-data-modeler
Source: https://github.com/Adgmed2018/visa/tree/main/agents/visa-data-modeler
Command: npx skills add https://github.com/Adgmed2018/visa --skill visa-data-modeler

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

O Skill propõe o esquema de dados prospectivo do produto a partir do domínio descoberto — entidades, relacionamentos, ERD e regras de integridade — antes de existir código, facilitando a comunicação com a equipe de engenharia e de dados.

Core Features & Use Cases

  • Modela entidades, relacionamentos e regras de negócio a partir do domínio mapeado.
  • Gera um ERD (Mermaid), dicionário de dados e constraints para validação inicial.
  • Documenta decisões de paradigma e lacunas para coleta, com foco em governança e rastreabilidade.

Quick Start

Comece definindo o domínio e permita que o modelo gere o esquema de dados prospectivo para implementação.

Frequently Asked Questions about visa-data-modeler

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I model a prospective data schema from domain insights before writing code?

To model a prospective data schema, you define the mapped domain and apply a modeling workflow to generate entities, relationships, ERD structures, and business rules before implementation begins.

What is the best way to generate an ER diagram and data dictionary from business rules?

Generating an ER diagram and data dictionary requires applying a modeling workflow to your domain constraints, which produces Mermaid ERD structures, data dictionaries, and integrity rules for engineering teams.

Can I document data governance and traceability for aggregate roots and constraints?

Documenting data governance and traceability involves recording constraints, aggregate roots, and deferred decisions, while clearly marking uncertainties to satisfy provenance requirements for coders.

Does domain-driven data modeling work for validating business constraints and decision records?

Domain-driven data modeling works for validation by applying business rules to the discovered domain, generating documentation artifacts like data dictionaries and decision records for engineering.

How do I outline entities and deferred decisions for a new data product?

Outlining entities and deferred decisions requires modeling the prospective schema from the discovered domain, documenting constraints, and marking uncertainties to facilitate communication with data teams.

When do I need to generate a data dictionary and DDL from domain-driven design?

You need to generate a data dictionary and DDL when modeling prospective schemas from domain insights, ensuring governance and traceability by documenting constraints, aggregate roots, and deferred decisions.