Does it cover advanced features like partitioning?

Yes, it includes guidance on range partitioning, materialized views, and other advanced strategies for managing and querying large-scale datasets efficiently.

Can it help with N+1 query problems?

Yes, it provides specific implementation patterns for both SQL-level JOINs and application-level batch loading to eliminate the N+1 query anti-pattern.

How does it assist with query plan analysis?

It helps interpret EXPLAIN and EXPLAIN ANALYZE output to identify Seq Scans, high-cost nodes, and opportunities for Index-Only scans to minimize I/O.

What databases does this skill support?

While the specific syntax examples focus heavily on PostgreSQL and MySQL, the core concepts of indexing, query plan analysis, and optimization patterns apply to most relational databases.

SQL Optimization Patterns

Name: SQL Optimization Patterns
Author: icartsh

byicartsh

Database Management

Optimizes database performance through advanced indexing strategies, query plan analysis, and efficient data retrieval patterns.

About

This skill empowers Claude to act as a database performance engineer, transforming sluggish queries into efficient operations. It provides deep expertise in analyzing EXPLAIN plans, implementing specialized indexing strategies (like B-Tree, GIN, and Partial indexes), and identifying architectural bottlenecks such as N+1 query patterns. Whether you are debugging production latency or designing a scalable schema, this skill provides the systematic guidance needed to ensure your database layer remains responsive, scalable, and cost-effective.

Key Features

0 GitHub stars
Advanced scaling strategies using partitioning and materialized views
Performance-first patterns for cursor-based pagination and aggregation
Structural techniques to identify and eliminate N+1 query anti-patterns
Strategic indexing recommendations including Composite, Partial, and GIN indexes
Deep EXPLAIN and EXPLAIN ANALYZE query plan interpretation

Use Cases

Refactoring slow-running legacy queries to improve application response times
Reducing database server load and infrastructure costs through query tuning
Designing high-performance database schemas for data-intensive applications

About

Key Features

0 GitHub stars
Advanced scaling strategies using partitioning and materialized views
Performance-first patterns for cursor-based pagination and aggregation
Structural techniques to identify and eliminate N+1 query anti-patterns
Strategic indexing recommendations including Composite, Partial, and GIN indexes
Deep EXPLAIN and EXPLAIN ANALYZE query plan interpretation

Use Cases

Refactoring slow-running legacy queries to improve application response times
Reducing database server load and infrastructure costs through query tuning
Designing high-performance database schemas for data-intensive applications