How does it help with large dataset pagination?

The skill provides patterns for implementing cursor-based pagination, which avoids the severe performance degradation seen when using large OFFSET values in standard SQL.

Does it provide advice on when to use specific indexes?

Yes, it covers when to use B-Tree, Hash, GIN, GiST, and BRIN indexes, as well as the performance benefits of partial and expression-based indexes.

Can this skill help with ORM performance issues?

Yes, it specifically addresses common ORM pitfalls like the N+1 query problem and provides strategies for batch loading and efficient JOIN usage.

What database systems does this skill support?

While the core optimization patterns are universal to SQL, this skill provides specific, detailed examples for PostgreSQL and MySQL execution plans and index types.

SQL Optimization Patterns

Name: SQL Optimization Patterns
Author: Microck

byMicrock

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Database Management

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

About

This skill provides Claude with expert-level knowledge in database performance tuning, enabling it to transform slow, inefficient SQL queries into high-performance operations. It covers critical concepts like EXPLAIN plan interpretation, strategic index creation (including B-Tree, GIN, and GiST), and the resolution of common anti-patterns such as N+1 queries and inefficient pagination. By using this skill, developers can reduce database load, improve application response times, and design schemas that scale effectively with growing datasets.

Key Features

High-performance pagination using cursor-based techniques
Optimization of batch operations, CTEs, and materialized views
Advanced indexing strategies including composite, partial, and covering indexes
Identification and resolution of N+1 query anti-patterns
Expert interpretation of EXPLAIN and EXPLAIN ANALYZE query plans
81 GitHub stars

Use Cases

Refactoring legacy SQL code to improve resource utilization and reduce costs
Debugging slow-running production database queries to reduce latency
Designing performant schemas for applications handling large-scale datasets

About

Key Features

High-performance pagination using cursor-based techniques
Optimization of batch operations, CTEs, and materialized views
Advanced indexing strategies including composite, partial, and covering indexes
Identification and resolution of N+1 query anti-patterns
Expert interpretation of EXPLAIN and EXPLAIN ANALYZE query plans
81 GitHub stars

Use Cases

Refactoring legacy SQL code to improve resource utilization and reduce costs
Debugging slow-running production database queries to reduce latency
Designing performant schemas for applications handling large-scale datasets