Is it suitable for time-series data analysis?

Yes, it covers advanced window functions like LAG, LEAD, and running totals which are essential for analyzing trends and changes over time.

Does it support different database dialects?

Yes, it includes specific syntax and patterns for PostgreSQL, MySQL, and SQLite, especially for operations like UPSERTs and conditional aggregations.

Can it help with hierarchical data structures?

Absolutely, it provides specialized recursive CTE patterns designed to navigate organizational charts, category trees, and other recursive data models.

How does this skill improve query performance?

It provides patterns for index-friendly queries, efficient keyset pagination, and batching techniques that minimize database locking and resource consumption.

SQL Patterns & Optimization

Name: SQL Patterns & Optimization
Author: majesticlabs-dev

bymajesticlabs-dev

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Database Management

Provides advanced SQL design patterns and optimization techniques for efficient data engineering and complex query construction.

About

The SQL Patterns skill equips Claude with specialized knowledge for architecting high-performance database queries and complex data transformations. It goes beyond basic CRUD operations to offer implementation guidance for Common Table Expressions (CTEs), window functions, recursive hierarchies, and database-specific UPSERT patterns. This skill is particularly useful for developers and data engineers looking to refactor slow queries, implement efficient pagination, or manage large-scale batch operations while maintaining index-friendly search patterns across PostgreSQL, MySQL, and SQLite.

Key Features

Complex conditional logic patterns using CASE expressions and lateral joins
Recursive CTE implementation for navigating hierarchical data structures
18 GitHub stars
Advanced Window Functions for analytical reporting and time-series analysis
Performance optimization techniques including keyset pagination and index-friendly queries
Database-specific UPSERT patterns for PostgreSQL, MySQL, and SQLite

Use Cases

Performing large-scale data migrations or batch updates without locking production tables
Refactoring legacy nested queries into readable and maintainable Common Table Expressions
Building efficient real-time analytics dashboards using window functions and materialized views

About

Key Features

Complex conditional logic patterns using CASE expressions and lateral joins
Recursive CTE implementation for navigating hierarchical data structures
18 GitHub stars
Advanced Window Functions for analytical reporting and time-series analysis
Performance optimization techniques including keyset pagination and index-friendly queries
Database-specific UPSERT patterns for PostgreSQL, MySQL, and SQLite

Use Cases

Performing large-scale data migrations or batch updates without locking production tables
Refactoring legacy nested queries into readable and maintainable Common Table Expressions
Building efficient real-time analytics dashboards using window functions and materialized views