Why is security context mandatory in Drizzle Cube?

Security context is required to ensure multi-tenant data isolation, preventing data leaks by forcing every query to filter through specific organizational or tenant IDs.

What is the primary purpose of the cube-definition skill?

It is designed to help you create semantic layer cubes that abstract database tables into business-friendly dimensions and measures using Drizzle ORM.

Can I create custom calculations using this skill?

Yes, you can define calculated measures using SQL expressions and create computed dimensions to transform raw data into useful business attributes.

Does this skill support TypeScript?

Absolutely. Since it uses Drizzle ORM as its foundation, all cubes, dimensions, and measures are fully type-safe with built-in IDE support.

Drizzle Cube Definition

Name: Drizzle Cube Definition
Author: cliftonc

bycliftonc

Gestión de Bases de Datos

Defines type-safe semantic layer cubes using Drizzle ORM for multi-tenant analytics and business logic abstraction.

Acerca de

The Drizzle Cube Definition skill enables developers to build powerful semantic layers directly over Drizzle ORM tables. It provides a structured way to define business-friendly dimensions and measures while enforcing mandatory security context filtering for robust multi-tenant data isolation. By abstracting complex database schemas into intuitive cubes, this skill simplifies the creation of analytics dashboards and reporting tools while maintaining full TypeScript type safety and support for computed metrics and complex aggregations.

Características Principales

Mandatory security context filtering for secure multi-tenant data isolation
0 GitHub stars
Advanced aggregation measures including count, sum, average, and custom calculated metrics
Type-safe semantic layer definitions using Drizzle ORM table references
Support for various dimension types including string, numeric, and time-based fields
Conditional aggregation through filtered measures and SQL-computed dimensions

Casos de Uso

Defining business logic abstractions over raw database tables for simplified reporting
Implementing complex KPI calculations and time-series data analysis
Building secure, multi-tenant analytics dashboards for SaaS applications

Acerca de

Características Principales

Mandatory security context filtering for secure multi-tenant data isolation
0 GitHub stars
Advanced aggregation measures including count, sum, average, and custom calculated metrics
Type-safe semantic layer definitions using Drizzle ORM table references
Support for various dimension types including string, numeric, and time-based fields
Conditional aggregation through filtered measures and SQL-computed dimensions

Casos de Uso

Defining business logic abstractions over raw database tables for simplified reporting
Implementing complex KPI calculations and time-series data analysis
Building secure, multi-tenant analytics dashboards for SaaS applications