Why does it prefer BIGINT over UUID for primary keys?

It follows the practice of using BIGINT GENERATED ALWAYS AS IDENTITY for superior performance and storage efficiency, recommending UUID only for distributed systems where global uniqueness is required.

Can it help with row-level security (RLS)?

Yes, it includes instructions for enabling RLS and creating access policies to secure your data at the database level for multi-tenant applications.

Does this skill support PostgreSQL-specific types like JSONB?

Yes, it provides detailed guidance on when and how to use JSONB, including GIN indexing for optimal search performance.

How does this help with database performance?

It implements best practices for indexing (B-tree, GIN, GiST), partitioning for large datasets, and specific configurations for update-heavy vs. insert-heavy workloads.

Does it handle schema migrations?

It focuses on the architectural design of tables and constraints, which can be directly used to generate high-quality, production-ready migration scripts.

PostgreSQL Table Design Expert

Name: PostgreSQL Table Design Expert
Author: HermeticOrmus

byHermeticOrmus

Database Management

Architects high-performance PostgreSQL schemas using industry-standard best practices for indexing, data types, and normalization.

About

This skill transforms Claude into a PostgreSQL specialist, providing comprehensive guidance on designing robust and scalable database schemas. It enforces critical architectural rules like 3NF normalization, proper use of constraints, and optimized indexing strategies including B-tree, GIN, and GiST. Whether you are handling time-series data with partitioning, managing JSONB for semi-structured attributes, or optimizing update-heavy tables with fillfactor adjustments, this skill ensures your database architecture is performant, durable, and follows modern PostgreSQL idioms.

Key Features

0 GitHub stars
Advanced indexing strategies (Partial, Expression, Covering, GIN/GiST)
Partitioning strategies for large-scale data (Range, List, Hash)
Comprehensive data type guidance (TIMESTAMPTZ, BIGINT, JSONB, etc.)
Schema normalization and performance-driven denormalization patterns
Constraint management including Row-Level Security and Exclusion constraints

Use Cases

Refactoring legacy databases to use modern PostgreSQL features like identity columns
Designing a new relational schema for a SaaS application from scratch
Optimizing existing slow queries by implementing advanced indexing or partitioning

About

Key Features

0 GitHub stars
Advanced indexing strategies (Partial, Expression, Covering, GIN/GiST)
Partitioning strategies for large-scale data (Range, List, Hash)
Comprehensive data type guidance (TIMESTAMPTZ, BIGINT, JSONB, etc.)
Schema normalization and performance-driven denormalization patterns
Constraint management including Row-Level Security and Exclusion constraints

Use Cases

Refactoring legacy databases to use modern PostgreSQL features like identity columns
Designing a new relational schema for a SaaS application from scratch
Optimizing existing slow queries by implementing advanced indexing or partitioning