normalized

Generate 3NF physical database schemas and data contracts from conceptual models.

1|Updated Feb 7, 2026
One-click install
npx skills add https://github.com/Semprini/md-ddl --skill normalized
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: normalized
Source: https://github.com/Semprini/md-ddl/tree/main/agents/agent-artifact/skills/normalized
Command: npx skills add https://github.com/Semprini/md-ddl --skill normalized

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the challenge of translating conceptual data models into practical, operational database schemas, specifically focusing on Third Normal Form (3NF) designs for OLTP systems.

Core Features & Use Cases

  • 3NF Schema Generation: Creates physical database DDL (e.g., PostgreSQL, SQL Server) from logical models.
  • Data Contract Outputs: Generates JSON Schema and Parquet schema contracts for data products.
  • Pragmatic Denormalization: Allows for intentional, documented deviations from strict 3NF to improve usability and performance.
  • Use Case: Generate a PostgreSQL DDL script for a customer domain model, including appropriate primary keys, foreign keys, and temporal columns, while allowing selective denormalization for frequently accessed descriptive attributes.

Quick Start

Generate a PostgreSQL DDL script for the customer domain model, applying pragmatic 3NF principles.

Frequently Asked Questions about normalized

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

FAQPage Schema
How do I generate a 3NF database schema from a conceptual data model?

This Skill creates 3NF database schemas by translating conceptual models into physical DDL for PostgreSQL, SQL Server, Snowflake, and Databricks SQL. It automatically handles primary keys, foreign keys, temporal data, enums, and inheritance during generation.

Can I generate JSON Schema and Parquet data contracts from my logical model?

You can generate JSON Schema and Parquet schema contracts directly from your logical models. These outputs define structured data products, ensuring schema consistency across OLTP systems and downstream data platforms.

Does this approach support selective denormalization for OLTP performance?

This approach supports selective denormalization for OLTP performance by allowing documented deviations from strict 3NF. It intentionally denormalizes frequently accessed descriptive attributes to improve query usability while maintaining overall schema integrity.

What is the best way to model temporal data and enums in a physical database DDL?

The best way to model temporal data and enums in a physical DDL is through pragmatic 3NF schema generation. It incorporates specialized handling for temporal columns and enum types directly into the database DDL creation process.

Can I use this to create DDL for both PostgreSQL and Databricks SQL?

You can use this to create DDL for both PostgreSQL and Databricks SQL, along with SQL Server and Snowflake. It generates platform-specific physical database schemas tailored to each system's syntax.

When should I not use strict Third Normal Form for database design?

You should not use strict Third Normal Form when query performance or usability suffers from excessive joins. This Skill applies pragmatic denormalization, allowing documented deviations to optimize frequently accessed descriptive attributes in OLTP systems.