What primary keys does this skill recommend?

It prefers BIGINT GENERATED ALWAYS AS IDENTITY for most tables, recommending UUID only when global uniqueness or opacity is strictly required.

How does the skill handle PostgreSQL indexing?

It provides rules for automatic PK/Unique indexing and specifically reminds developers to manually index foreign key columns, as PostgreSQL does not do this by default.

Why avoid the SERIAL data type?

The skill follows modern SQL standards by recommending 'GENERATED ALWAYS AS IDENTITY' over the legacy SERIAL type for better compatibility and behavior.

Does it support modern PostgreSQL features?

Yes, it includes guidance for PG15+ features like 'NULLS NOT DISTINCT', JSONB indexing, vector types via pgvector, and declarative partitioning.

PostgreSQL Table Design

Name: PostgreSQL Table Design
Author: HermeticOrmus

byHermeticOrmus

Database Management

Architects optimized, production-ready PostgreSQL schemas using industry best practices for data integrity and performance.

About

The PostgreSQL Table Design skill provides Claude with specialized knowledge for creating robust database architectures. It enforces strict standards for normalization (3NF), data type selection, and indexing strategies to ensure long-term scalability and performance. From implementing Row-Level Security and complex partitioning to avoiding common pitfalls like unindexed foreign keys or silent coercions, this skill guides developers through the nuances of PostgreSQL-specific features like JSONB, GIN/GiST indexes, and transactional DDL.

Key Features

Expert data type selection including TIMESTAMPTZ, BIGINT, and JSONB optimization.
Comprehensive indexing strategies for B-tree, GIN, GiST, and partial indexes.
Advanced constraint management including PKs, FKs, and exclusion constraints.
Performance patterns for update-heavy and insert-heavy workloads.
Built-in guidance for table partitioning and Row-Level Security (RLS) implementation.
0 GitHub stars

Use Cases

Designing a high-performance relational schema for a new enterprise application.
Implementing complex data structures using JSONB, arrays, or range types with efficient indexing.
Optimizing existing PostgreSQL tables for better query execution and reduced bloat.

About

Key Features

Expert data type selection including TIMESTAMPTZ, BIGINT, and JSONB optimization.
Comprehensive indexing strategies for B-tree, GIN, GiST, and partial indexes.
Advanced constraint management including PKs, FKs, and exclusion constraints.
Performance patterns for update-heavy and insert-heavy workloads.
Built-in guidance for table partitioning and Row-Level Security (RLS) implementation.
0 GitHub stars

Use Cases

Designing a high-performance relational schema for a new enterprise application.
Implementing complex data structures using JSONB, arrays, or range types with efficient indexing.
Optimizing existing PostgreSQL tables for better query execution and reduced bloat.