Forge-Data-Modelling

Defines entities, relationships, ownership, and lifecycle rules for system data models.

Updated Jul 18, 2026
One-click install
npx skills add https://github.com/Nealsch/ForgeOS --skill forge-data-modelling-nealsch
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Forge-Data-Modelling
Source: https://github.com/Nealsch/ForgeOS/tree/main/Framework/05-Skills/02-Architecture/Forge-Data-Modelling
Command: npx skills add https://github.com/Nealsch/ForgeOS --skill forge-data-modelling-nealsch

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Software projects often embed data structures in code before anyone has agreed on what information the system manages, who owns it, or what rules govern it. This Skill provides a structured method to design conceptual, logical, and physical data models before implementation begins. ## Core Features & Use Cases - Entity and Relationship Definition: Identify business entities, define one-to-one, one-to-many, and many-to-many relationships, and establish clear data ownership. - Business Rules and Lifecycle Capture: Document data constraints, valid states, and creation-to-archival lifecycle requirements. - Template-Driven Documentation: Produce consistent data model documents using the Data-Model template aligned with architecture and glossary templates. - Use Case: When designing a new order management system, use this Skill to model customers, orders, and products, define their relationships and ownership, and capture validation rules before any database schema is written. ## Quick Start Ask the AI to apply Forge-Data-Modelling to create a data model for your feature, listing entities, relationships, ownership, and business rules based on your requirements.

Frequently Asked Questions about Forge-Data-Modelling

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

FAQPage Schema
How do I create a data model before writing database code?▼

Start by identifying important information concepts and entities rather than tables or fields. Then define relationships, capture business rules and constraints, map the data lifecycle, and validate the model against requirements before any implementation.

What is the difference between conceptual, logical, and physical data models?▼

A conceptual model describes major business concepts and relationships. A logical model adds attributes, rules, and constraints showing how information is organized. A physical model covers storage implementation and database-specific technical details.

When should I not use data modelling?▼

Skip data modelling when the task has no meaningful data impact, such as simple implementation changes, writing queries against an already understood model, or performing operational database administration tasks.

Does data modelling choose database technologies or write SQL?▼

No. Data modelling defines entities, relationships, ownership, and rules only. Selecting database technologies, writing SQL queries, and managing database infrastructure are separate engineering responsibilities handled downstream.

How does a data model support security and privacy analysis?▼

A documented data model identifies what information exists and who owns it, which lets security reviews locate sensitive data and define protection requirements. It provides the foundation but does not replace dedicated security analysis.