Does it offer advice on indexing beyond simple single-column indexes?

Absolutely. It covers advanced indexing strategies including composite indexes (where column order matters), partial indexes for filtered data, and expression-based indexes.

What database engines are supported by this skill?

The skill provides general SQL optimization principles applicable to PostgreSQL, MySQL, and SQL Server, including specific syntax for common engines like PostgreSQL.

How does it assist with EXPLAIN plans?

It helps interpret output metrics such as sequential scans, index scans, cost estimates, and actual execution times to pinpoint the exact cause of slowness.

Can it help me fix N+1 query problems?

Yes, it provides specific strategies for converting multiple sequential queries into efficient JOIN operations or single-batch requests.

SQL Query Optimizer

Name: SQL Query Optimizer
Author: armanzeroeight

byarmanzeroeight

•

Database Management

Optimizes SQL database performance through advanced indexing strategies, query rewriting, and execution plan analysis.

About

The Query Optimizer skill empowers developers to diagnose and resolve database performance bottlenecks by providing expert guidance on SQL optimization. It assists with interpreting execution plans via EXPLAIN commands, identifying missing or inefficient indexes, and refactoring slow queries to improve throughput. Whether you are dealing with N+1 query problems, slow aggregations, or inefficient pagination, this skill provides the implementation patterns and best practices needed to maintain a high-performance database layer.

Key Features

Execution plan analysis and bottleneck identification using EXPLAIN ANALYZE
SQL refactoring to eliminate N+1 queries and inefficient subqueries
Strategic indexing recommendations including composite, partial, and covering indexes
Pagination optimization using cursor-based patterns for large datasets
Database monitoring setup and index usage tracking
19 GitHub stars

Use Cases

Diagnosing and fixing slow-running production queries that cause application latency
Designing optimal indexing schemas for new tables to ensure scalability
Refactoring legacy SQL scripts to improve resource efficiency and reduce database load

About

Key Features

Execution plan analysis and bottleneck identification using EXPLAIN ANALYZE
SQL refactoring to eliminate N+1 queries and inefficient subqueries
Strategic indexing recommendations including composite, partial, and covering indexes
Pagination optimization using cursor-based patterns for large datasets
Database monitoring setup and index usage tracking
19 GitHub stars

Use Cases

Diagnosing and fixing slow-running production queries that cause application latency
Designing optimal indexing schemas for new tables to ensure scalability
Refactoring legacy SQL scripts to improve resource efficiency and reduce database load