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.