How does this skill help with PostgreSQL performance?

It provides specific recommendations for indexing strategies (like B-tree vs GIN), partitioning for large datasets, and choosing efficient data types to minimize storage and maximize query speed.

Does it support advanced Postgres features like JSONB or RLS?

Absolutely. The skill covers modern features including JSONB indexing, Row-Level Security policies, and specialized types like Ranges, Arrays, and Network types.

Why use this instead of generic SQL guidance?

PostgreSQL has specific behaviors regarding case sensitivity, identity columns, and MVCC storage. This skill provides domain-specific expertise tailored to these nuances that generic SQL tools often miss.

Can it help with safe schema migrations?

Yes, it includes guidelines for transactional DDL and concurrent index creation to ensure database updates don't lock your tables or cause downtime.

PostgreSQL Schema Design Expert

Name: PostgreSQL Schema Design Expert
Author: francanete

byfrancanete

Database Management

Architects high-performance PostgreSQL databases using industry-standard schema design, indexing strategies, and data integrity patterns.

About

The PostgreSQL Schema Design Expert skill provides Claude with deep domain expertise for building and optimizing PostgreSQL databases. It offers comprehensive guidance on everything from selecting the correct data types (like TIMESTAMPTZ vs TIMESTAMP) to implementing advanced indexing techniques such as GIN, GiST, and BRIN. This skill is essential for developers looking to ensure their database architecture is scalable, performant, and follows 3rd Normal Form (3NF) principles while effectively managing modern features like JSONB, Row-Level Security, and declarative partitioning.

Key Features

Best practices for safe schema evolution and transactional DDL
Expert data type selection and PostgreSQL-specific 'gotcha' avoidance
Comprehensive indexing strategies including composite, partial, and expression indexes
0 GitHub stars
Advanced partitioning patterns for high-volume time-series and event data
Implementation of Row-Level Security (RLS) and complex constraints

Use Cases

Refactoring existing SQL structures to resolve performance bottlenecks
Designing production-ready database schemas for new applications
Implementing specialized data storage for JSONB documents or vector embeddings

About

Key Features

Best practices for safe schema evolution and transactional DDL
Expert data type selection and PostgreSQL-specific 'gotcha' avoidance
Comprehensive indexing strategies including composite, partial, and expression indexes
0 GitHub stars
Advanced partitioning patterns for high-volume time-series and event data
Implementation of Row-Level Security (RLS) and complex constraints

Use Cases

Refactoring existing SQL structures to resolve performance bottlenecks
Designing production-ready database schemas for new applications
Implementing specialized data storage for JSONB documents or vector embeddings