Does it follow a specific SQL style guide?

The skill promotes clean, readable code by emphasizing CTEs over nested subqueries and recommending explicit column selection over SELECT *.

Can this skill help optimize slow database queries?

Yes, it includes best practices for index awareness and using EXPLAIN statements to analyze and improve query performance.

Which databases is this compatible with?

The patterns are based on standard SQL and are highly compatible with major relational databases like PostgreSQL, MySQL, and SQL Server.

Is it suitable for business intelligence tasks?

Absolutely. It is specifically designed for metrics calculation, cohort analysis, and generating complex business reports.

What kind of SQL operations does this skill support?

It supports a wide range of advanced operations including window functions (LAG/LEAD, running totals), complex joins, aggregations, and Common Table Expressions (CTEs).

SQL Data Analysis

Name: SQL Data Analysis
Author: spjoshis

byspjoshis

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Data Science & ML

Performs complex data extraction and sophisticated business analysis using advanced SQL techniques and performance-optimized queries.

About

The SQL Analysis skill enables Claude to handle advanced data exploration and business reporting tasks through the mastery of complex SQL operations. It provides structured patterns for implementing window functions, multi-table joins, and Common Table Expressions (CTEs) to solve intricate analytical problems such as cohort retention and funnel analysis. By incorporating best practices like query plan optimization and readable SQL styling, this skill ensures that data-driven insights are both accurate and maintainable within any development workflow.

Key Features

1 GitHub stars
Data Cohort and Customer Segmentation analysis patterns
Complex Multi-table Joins for deep data extraction
Performance Optimization using EXPLAIN and index awareness
Advanced Window Functions for running totals and growth metrics
Common Table Expressions (CTEs) for readable query logic

Use Cases

Performing customer retention and churn analysis using cohorts
Optimizing slow-running legacy SQL queries for better performance
Generating automated monthly revenue and business growth reports

About

Key Features

1 GitHub stars
Data Cohort and Customer Segmentation analysis patterns
Complex Multi-table Joins for deep data extraction
Performance Optimization using EXPLAIN and index awareness
Advanced Window Functions for running totals and growth metrics
Common Table Expressions (CTEs) for readable query logic

Use Cases

Performing customer retention and churn analysis using cohorts
Optimizing slow-running legacy SQL queries for better performance
Generating automated monthly revenue and business growth reports