What databases does this skill support?

While many patterns are universal to SQL, it provides specific optimization techniques and syntax for PostgreSQL and MySQL, including detailed EXPLAIN plan analysis.

Does this skill cover advanced indexing?

Absolutely. It covers standard B-Tree indexes as well as specialized types like GIN, GiST, and BRIN, along with functional, partial, and covering index strategies.

Can I use it for database schema design?

Yes, the skill includes patterns for table partitioning and materialized views to help you design performant and scalable schemas from the beginning.

Does it explain how to interpret query costs?

Yes, it provides a breakdown of key metrics in execution plans such as Seq Scans, Index Scans, and estimated vs. actual row counts to help identify bottlenecks.

Can it help with the N+1 query problem?

Yes, it identifies N+1 anti-patterns and provides refactoring strategies using JOINs, window functions, or batch loading to minimize database round trips.

SQL Performance Optimizer

Name: SQL Performance Optimizer
Author: Microck

byMicrock

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데이터베이스 관리

Optimizes SQL queries and database schemas using advanced indexing strategies and execution plan analysis.

소개

This skill empowers Claude to transform slow database operations into high-performance queries by applying systematic optimization patterns. It provides expert guidance on interpreting EXPLAIN plans, implementing complex indexing strategies—including composite, partial, and covering indexes—and resolving common bottlenecks like N+1 queries and inefficient pagination. Whether you are designing a new schema from scratch or debugging legacy production queries, this skill helps reduce latency, lower infrastructure costs, and ensure your application scales effectively as data grows.

주요 기능

Advanced EXPLAIN and EXPLAIN ANALYZE output interpretation
Implementation of diverse indexing strategies (B-Tree, GIN, GiST, BRIN)
Optimized pagination techniques using cursor-based logic instead of OFFSET
89 GitHub stars
Query refactoring to eliminate N+1 anti-patterns and Cartesian products
Advanced schema optimization using Materialized Views and Table Partitioning

사용 사례

Designing high-performance database schemas for scalable applications
Debugging and fixing slow-running production database queries
Reducing database CPU load and cloud costs through query efficiency

소개

주요 기능

Advanced EXPLAIN and EXPLAIN ANALYZE output interpretation
Implementation of diverse indexing strategies (B-Tree, GIN, GiST, BRIN)
Optimized pagination techniques using cursor-based logic instead of OFFSET
89 GitHub stars
Query refactoring to eliminate N+1 anti-patterns and Cartesian products
Advanced schema optimization using Materialized Views and Table Partitioning

사용 사례

Designing high-performance database schemas for scalable applications
Debugging and fixing slow-running production database queries
Reducing database CPU load and cloud costs through query efficiency