data-schema-knowledge-modeling

# Data Schema & Knowledge Modeling

## Table of Contents
- [Purpose](#purpose)
- [When to Use](#when-to-use)
- [What Is It](#what-is-it)
- [Workflow](#workflow)
- [Schema Types](#schema-types)
- [Common Patterns](#common-patterns)
- [Guardrails](#guardrails)
- [Quick Reference](#quick-reference)

## Purpose

Create rigorous, validated models of entities, relationships, and constraints that enable correct system implementation, knowledge representation, and semantic reasoning.

## When to Use

**Invoke this skill when you need to:**
- Design database schema (SQL, NoSQL, graph) for new application
- Model complex domain with many entities and relationships
- Build knowledge graph or ontology for semantic search/reasoning
- Define API data models and contracts
- Create taxonomies or classification hierarchies
- Establish data governance and canonical models
- Migrate legacy schemas to modern architectures
- Resolve ambiguity in domain concepts and relationships
- Enable data integration across systems
- Document system invariants and business rules

**Common trigger phrases:**
- "Design a schema for..."
- "Model the entities and relationships"
- "Create a knowledge graph"
- "What's the data model?"
- "Define the ontology"
- "How should we structure this data?"
- "Map relationships between..."
- "Design the API data model"

## What Is It

**Data schema & knowledge modeling** is the process of formally defining:

1. **Entities** - Things that exist (User, Product, Order, Organization)
2. **Attributes** - Properties of entities (name, price, status, createdAt)
3. **Relationships** - Connections between entities (User owns Order, Product belongsTo Category)
4. **Constraints** - Rules and invariants (unique email, price > 0, one primary address)
5. **Cardinality** - How many of each (one-to-many, many-to-many)

**Quick example:** E-commerce schema:
- **Entities**: User, Product, Order, Cart, Payment
- **Relationships**: User has many Orders, Order contains many Products (vi