Does it provide help with specific index types?

Yes, it covers a wide range of indexes including B-Tree, GIN for JSONB/Search, GiST for geometric data, and BRIN for very large datasets.

What databases does this skill support?

While many patterns are universal, this skill features specialized optimization techniques for PostgreSQL and MySQL, including specific EXPLAIN syntax and advanced index types.

How does it help with large table performance?

It provides strategies for managing scale, such as replacing slow OFFSET pagination with cursor-based logic, implementing table partitioning, and using materialized views for expensive aggregations.

Can this skill help fix N+1 query issues in my code?

Yes, it identifies N+1 query anti-patterns in application logic and provides specific refactoring patterns using JOINs or batch loading to consolidate database hits.

SQL Optimization Patterns

Name: SQL Optimization Patterns
Author: wshobson

bywshobson

•

23,194

Gestión de Bases de Datos

Optimizes SQL queries and database schemas through advanced indexing strategies and systematic execution plan analysis.

Acerca de

This skill empowers developers to transform sluggish database operations into high-performance queries by applying industry-standard optimization patterns. It provides Claude with specialized knowledge in interpreting PostgreSQL EXPLAIN plans, implementing sophisticated indexing (B-Tree, GIN, GiST), eliminating N+1 query anti-patterns, and managing large-scale data through partitioning and materialized views. Whether you're debugging production bottlenecks or architecting a new schema, this skill ensures your database layer remains scalable, cost-effective, and responsive by focusing on execution efficiency and reduced resource consumption.

Características Principales

High-performance pagination patterns using cursor-based navigation
23,194 GitHub stars
Remediation of N+1 query anti-patterns through JOINs and batch loading techniques
In-depth analysis of EXPLAIN and EXPLAIN ANALYZE query plans to identify bottlenecks
Database architecture guidance for partitioning, materialized views, and subquery optimization
Implementation of advanced indexing strategies including composite, partial, and covering indexes

Casos de Uso

Detecting and fixing inefficient query patterns in application code during the development cycle
Designing high-performance database schemas for applications with rapidly growing datasets
Refactoring slow-running production queries to reduce latency and database server load

Acerca de

Características Principales

High-performance pagination patterns using cursor-based navigation
23,194 GitHub stars
Remediation of N+1 query anti-patterns through JOINs and batch loading techniques
In-depth analysis of EXPLAIN and EXPLAIN ANALYZE query plans to identify bottlenecks
Database architecture guidance for partitioning, materialized views, and subquery optimization
Implementation of advanced indexing strategies including composite, partial, and covering indexes

Casos de Uso

Detecting and fixing inefficient query patterns in application code during the development cycle
Designing high-performance database schemas for applications with rapidly growing datasets
Refactoring slow-running production queries to reduce latency and database server load