How does it assist with query plan analysis?

It provides guidance on interpreting EXPLAIN output, helping you identify sequential scans, high-cost joins, and opportunities for index-only scans.

Does this skill help with PostgreSQL and MySQL?

Yes, while many patterns are universal to SQL, it includes specific syntax and optimization strategies for both PostgreSQL and MySQL.

Does it cover advanced scaling techniques?

Yes, it includes patterns for high-level optimizations like table partitioning, materialized views, and concurrent index rebuilding.

Can it help me fix N+1 query problems?

Absolutely. The skill provides specific code patterns to transform N+1 query loops into efficient batch loads or JOIN-based operations.

What is the SQL Optimization Patterns skill?

It is a specialized capability for Claude Code that provides patterns for query tuning, advanced indexing strategies, and database performance optimization.

SQL Optimization Patterns

Name: SQL Optimization Patterns
Author: Activer007

byActiver007

Database Management

Optimizes database performance through advanced query tuning, indexing strategies, and detailed execution plan analysis.

About

This Claude Code Skill provides a comprehensive framework for transforming slow database operations into high-performance queries. It offers deep expertise in interpreting execution plans (EXPLAIN/ANALYZE), implementing advanced indexing strategies such as partial and covering indexes, and refactoring common performance bottlenecks like N+1 queries and inefficient pagination. Designed for developers and system administrators, this skill helps reduce database load, lower infrastructure costs, and ensure application scalability across PostgreSQL, MySQL, and other SQL environments.

Key Features

Advanced indexing patterns including B-Tree, GIN, GiST, and BRIN indexes
Expert interpretation of EXPLAIN and ANALYZE query execution plans
Advanced scaling techniques including Materialized Views and Table Partitioning
0 GitHub stars
Strategies to identify and eliminate N+1 query anti-patterns
Cursor-based pagination patterns to replace slow OFFSET methods

Use Cases

Optimizing application response times by reducing database-level latency
Designing performant database schemas for large-scale applications
Debugging and refactoring slow-running production database queries

About

Key Features

Advanced indexing patterns including B-Tree, GIN, GiST, and BRIN indexes
Expert interpretation of EXPLAIN and ANALYZE query execution plans
Advanced scaling techniques including Materialized Views and Table Partitioning
0 GitHub stars
Strategies to identify and eliminate N+1 query anti-patterns
Cursor-based pagination patterns to replace slow OFFSET methods

Use Cases

Optimizing application response times by reducing database-level latency
Designing performant database schemas for large-scale applications
Debugging and refactoring slow-running production database queries