Is this skill specific to a particular database engine?

While it features specific examples for PostgreSQL and MySQL, the core optimization concepts and patterns are applicable across most relational database management systems (RDBMS).

Can this skill help with the N+1 query problem?

Yes, it includes specific strategies for converting N+1 query patterns into efficient JOIN operations or batch loading techniques to minimize database round-trips.

Does it support advanced indexing like GIN or BRIN?

The skill covers a wide range of indexing strategies, including B-Tree, GIN for full-text and JSONB, BRIN for large tables, and covering indexes to maximize performance.

How does this skill help with slow database queries?

It provides systematic patterns for analyzing EXPLAIN plans to identify bottlenecks like full table scans and recommends specific indexing or query refactoring solutions to improve execution speed.

SQL Optimization Patterns

Name: SQL Optimization Patterns
Author: Microck

byMicrock

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Database Management

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

About

SQL Optimization Patterns is a specialized capability designed to transform slow database operations into high-performance queries. This skill equips Claude with the expertise to interpret EXPLAIN query plans, implement sophisticated indexing strategies—including B-Tree, GIN, and covering indexes—and refactor inefficient code patterns like the N+1 query problem. It is an essential resource for developers looking to reduce database load, improve application scalability, and design performant schemas for large-scale data sets.

Key Features

Proven patterns to eliminate N+1 query anti-patterns through batching and JOINs
EXPLAIN and ANALYZE query plan interpretation for bottleneck identification
Advanced indexing strategies including composite, partial, and functional indexes
81 GitHub stars
Performance-oriented pagination techniques using cursor-based logic
Advanced optimization via Materialized Views and table partitioning

Use Cases

Designing high-performance schemas for applications with growing datasets
Debugging and resolving slow-running production database queries
Optimizing application response times and reducing cloud database costs

About

Key Features

Proven patterns to eliminate N+1 query anti-patterns through batching and JOINs
EXPLAIN and ANALYZE query plan interpretation for bottleneck identification
Advanced indexing strategies including composite, partial, and functional indexes
81 GitHub stars
Performance-oriented pagination techniques using cursor-based logic
Advanced optimization via Materialized Views and table partitioning

Use Cases

Designing high-performance schemas for applications with growing datasets
Debugging and resolving slow-running production database queries
Optimizing application response times and reducing cloud database costs