Does this skill work with PostgreSQL and MySQL?

Yes, it covers universal SQL optimization concepts while providing specific implementation patterns for major databases like PostgreSQL and MySQL.

How does it help with N+1 query problems?

The skill provides specific refactoring patterns to transform iterative database calls into efficient JOINs or batch loading strategies to minimize round-trips.

Can this help me understand EXPLAIN ANALYZE output?

Absolutely. It breaks down key metrics in execution plans such as Seq Scans, Index Scans, and Join methods to help you identify the root cause of slow queries.

What is the SQL Optimization Patterns skill?

It is a specialized capability for Claude Code that provides expert guidance on improving database performance through query tuning, advanced indexing, and architectural patterns.

SQL Optimization Patterns

Name: SQL Optimization Patterns
Author: Activer007

byActiver007

数据库管理

Optimizes database performance using advanced indexing, EXPLAIN plan analysis, and efficient query design patterns.

关于

This skill empowers developers to transform slow database queries into lightning-fast operations through systematic optimization and rigorous analysis. It covers essential techniques including deciphering EXPLAIN plans, implementing advanced indexing strategies (B-Tree, GIN, Partial), and refactoring common performance killers like N+1 queries and high-offset pagination. By integrating these patterns into your workflow, you can significantly reduce database load, lower cloud costs, and improve overall application scalability for growing datasets.

主要功能

In-depth query execution plan (EXPLAIN) analysis
0 GitHub stars
Materialized views and table partitioning for large-scale data management
Advanced indexing strategies including Partial, Composite, and GIN indexes
Cursor-based pagination for high-performance data retrieval
Patterns for eliminating N+1 query bottlenecks

使用场景

Optimizing backend API response times by reducing database latency
Debugging and fixing slow-running production database queries
Designing high-performance schemas for data-intensive applications

关于

主要功能

In-depth query execution plan (EXPLAIN) analysis
0 GitHub stars
Materialized views and table partitioning for large-scale data management
Advanced indexing strategies including Partial, Composite, and GIN indexes
Cursor-based pagination for high-performance data retrieval
Patterns for eliminating N+1 query bottlenecks

使用场景

Optimizing backend API response times by reducing database latency
Debugging and fixing slow-running production database queries
Designing high-performance schemas for data-intensive applications