Does it support Supabase-specific features?

Yes, many patterns are based on Supabase best practices, including specific optimizations for Row Level Security (RLS) and efficient auth-based policies.

How does this skill improve PostgreSQL performance?

It provides specific guidance on advanced indexing (GIN, BRIN, Composite), identifies table bloat, and offers configuration templates to optimize memory usage and connection limits.

Can it help with identifying slow database queries?

Absolutely. It includes queries for analyzing pg_stat_statements to find the most expensive operations and provides patterns for Keyset Pagination to replace slow OFFSET methods.

What security features does this skill provide?

It focuses on Row Level Security (RLS) optimization, secure schema defaults, and best practices for isolating user data in multi-tenant environments.

PostgreSQL Database Patterns

Name: PostgreSQL Database Patterns
Author: flatrick

byflatrick

0•

Gestión de Bases de Datos

Optimizes PostgreSQL database schemas, queries, and security through expert-level performance patterns and implementation best practices.

PostgreSQL Database Patterns is a specialized capability for Claude Code designed to streamline database development and optimization. Based on Supabase best practices, this skill provides developers with immediate access to high-performance indexing strategies, optimized data types, and secure Row Level Security (RLS) patterns. Whether you are troubleshooting slow queries, designing a complex schema from scratch, or setting up production-ready configurations, this skill helps ensure your database layer is scalable, secure, and efficient. It also includes automated anti-pattern detection to find unindexed foreign keys and table bloat before they impact performance.

Características Principales

01Advanced indexing cheat sheets for B-tree, GIN, BRIN, and Partial indexes

02Optimized Row Level Security (RLS) policy templates

030 GitHub stars

04Performance-tuned data type recommendations for IDs, money, and timestamps

05Detection of common anti-patterns like unindexed foreign keys and table bloat

06Configuration templates for connection limits, timeouts, and monitoring

Casos de Uso

01Designing scalable schemas for multi-tenant applications using RLS

02Optimizing slow SQL queries and improving database migration efficiency

03Implementing high-performance pagination and queue processing patterns

Características Principales

01Advanced indexing cheat sheets for B-tree, GIN, BRIN, and Partial indexes

02Optimized Row Level Security (RLS) policy templates

030 GitHub stars

04Performance-tuned data type recommendations for IDs, money, and timestamps

05Detection of common anti-patterns like unindexed foreign keys and table bloat

06Configuration templates for connection limits, timeouts, and monitoring

Casos de Uso

01Designing scalable schemas for multi-tenant applications using RLS

02Optimizing slow SQL queries and improving database migration efficiency

03Implementing high-performance pagination and queue processing patterns