Which indexing types are covered?

It provides guidance on B-tree, GIN, BRIN, composite, partial, and covering indices for various data types including JSONB and timestamps.

Does this skill support Supabase-specific features?

Yes, it includes best practices for Row Level Security (RLS) and indexing patterns frequently used in the Supabase ecosystem.

Is this skill useful for backend developers?

Yes, it bridges the gap between application code and database performance, providing optimized SQL snippets for common tasks like UPSERT and pagination.

Can it help me fix slow database queries?

Absolutely; it includes anti-pattern detection scripts and an index cheat sheet to help identify and resolve performance bottlenecks.

PostgreSQL Patterns & Optimization

Name: PostgreSQL Patterns & Optimization
Author: 0x4139

by0x4139

0•

データベース管理

Optimizes PostgreSQL databases with best-practice schema designs, indexing strategies, and performance tuning patterns.

This skill provides Claude with a comprehensive reference for high-performance PostgreSQL development, drawing on production-tested practices from the Supabase ecosystem. It enables the AI to implement advanced indexing techniques (like GIN and BRIN), design secure Row Level Security (RLS) policies, and apply efficient query patterns such as cursor pagination and 'skip locked' job queues. Whether you are building a new schema or troubleshooting slow-running queries, this skill ensures your database is scalable, secure, and optimized for high-concurrency production workloads.

主な機能

01Advanced Indexing Reference (B-tree, GIN, BRIN, Partial, and Covering indices)

020 GitHub stars

03Optimized Row Level Security (RLS) Policy Implementation

04Standardized Data Type Selection and Configuration Templates

05Efficient Pagination and Concurrent Queue Processing Patterns

06Performance Tuning and Anti-Pattern Detection scripts

ユースケース

01Designing a scalable database schema for high-traffic web applications.

02Implementing fine-grained access control using Supabase-style RLS policies.

03Refactoring slow SQL queries and identifying missing foreign key indices.

主な機能

01Advanced Indexing Reference (B-tree, GIN, BRIN, Partial, and Covering indices)

020 GitHub stars

03Optimized Row Level Security (RLS) Policy Implementation

04Standardized Data Type Selection and Configuration Templates

05Efficient Pagination and Concurrent Queue Processing Patterns

06Performance Tuning and Anti-Pattern Detection scripts

ユースケース

01Designing a scalable database schema for high-traffic web applications.

02Implementing fine-grained access control using Supabase-style RLS policies.

03Refactoring slow SQL queries and identifying missing foreign key indices.