Does it include database administration tasks?

Yes, it covers production patterns like connection pooling with pgBouncer, managing table bloat with VACUUM, and monitoring query stats via pg_stat_statements.

How does it handle complex data patterns?

It provides implementation patterns for complex scenarios including gap-and-island detection, moving averages, and Slowly Changing Dimensions (SCD).

What databases does this skill focus on?

While the SQL fundamentals apply broadly, this skill emphasizes modern production environments like PostgreSQL 16+ and MySQL 8.0+.

Can it help me fix slow queries?

Yes, it provides advanced techniques for interpreting EXPLAIN ANALYZE output and implementing specific indexing strategies to resolve performance bottlenecks.

SQL & Relational Databases Mastery

Name: SQL & Relational Databases Mastery
Author: pluginagentmarketplace

bypluginagentmarketplace

•

Database Management

Master production-grade SQL optimization, schema design, and database performance tuning for data engineering workflows.

About

This skill equips Claude with advanced knowledge of relational databases, specifically focusing on production-grade environments like PostgreSQL 16+ and MySQL 8.0. It enables Claude to assist with complex SQL query writing using modern techniques like CTEs and window functions, architecting scalable schemas via partitioning and star schemas, and identifying performance bottlenecks using EXPLAIN ANALYZE. Whether you are troubleshooting lock contention, configuring pgBouncer for connection pooling, or implementing Slowly Changing Dimensions (SCD), this skill provides the domain-specific expertise required to build and maintain robust data infrastructure.

Key Features

Production-grade indexing strategies including GIN, partial, and covering indexes
Advanced SQL query optimization using EXPLAIN ANALYZE and execution plan analysis
Modern window functions and CTEs for complex analytical data processing
Database scaling techniques including range and list partitioning
Schema design patterns for data engineering such as Star Schema and SCD Type 2
1 GitHub stars

Use Cases

Optimizing slow-running analytical queries in production PostgreSQL environments
Designing scalable database schemas for high-traffic data engineering pipelines
Troubleshooting database performance issues like lock contention and table bloat

About

Key Features

Production-grade indexing strategies including GIN, partial, and covering indexes
Advanced SQL query optimization using EXPLAIN ANALYZE and execution plan analysis
Modern window functions and CTEs for complex analytical data processing
Database scaling techniques including range and list partitioning
Schema design patterns for data engineering such as Star Schema and SCD Type 2
1 GitHub stars

Use Cases

Optimizing slow-running analytical queries in production PostgreSQL environments
Designing scalable database schemas for high-traffic data engineering pipelines
Troubleshooting database performance issues like lock contention and table bloat