How does the skill analyze query bottlenecks?

It uses EXPLAIN ANALYZE to inspect the query execution plan, identifying high-cost nodes, sequential scans on large tables, and discrepancies between estimated and actual row counts.

Can this skill help fix performance issues in ORMs?

Yes, it includes specific logic to detect and resolve N+1 query problems in popular frameworks like Django, SQLAlchemy, Sequelize, and TypeORM.

Does it suggest specific index types?

Yes, it can recommend B-Tree indexes, composite indexes for specific query patterns, partial indexes for filtered datasets, and covering indexes to minimize table lookups.

Can I use this for MongoDB aggregation pipelines?

Yes, the skill includes support for MongoDB's executionStats to identify COLLSCAN stages and suggest appropriate multikey or compound indexes.

Which database engines are supported by this skill?

The skill provides specific optimization strategies, EXPLAIN plan analysis, and indexing suggestions for PostgreSQL, MySQL, and MongoDB.

Database Query Optimizer

Name: Database Query Optimizer
Author: Dexploarer

byDexploarer

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Gestión de Bases de Datos

Analyzes and optimizes database queries for PostgreSQL, MySQL, and MongoDB using EXPLAIN plans and automated index recommendations.

Acerca de

The Database Query Optimizer is a specialized performance skill for Claude Code that identifies and resolves database bottlenecks. It provides deep analysis of execution plans, detects inefficient N+1 query patterns in popular ORMs like Django and Sequelize, and suggests precise indexing strategies to reduce query latency. By interpreting EXPLAIN ANALYZE output and identifying red flags like sequential scans or high-cost operations, this skill helps developers refactor slow queries into high-performance SQL and NoSQL operations.

Características Principales

Comprehensive EXPLAIN and EXPLAIN ANALYZE plan interpretation
2 GitHub stars
Intelligent index suggestions including composite, partial, and covering indexes
Performance monitoring scripts for PostgreSQL, MySQL, and MongoDB
Query refactoring strategies to eliminate subqueries and optimize joins
Automated detection and fixing of N+1 query patterns in various ORMs

Casos de Uso

Optimizing application data layers to prevent database over-fetching
Designing efficient indexing strategies for new or existing schemas
Troubleshooting and fixing slow-running production queries

Acerca de

Características Principales

Comprehensive EXPLAIN and EXPLAIN ANALYZE plan interpretation
2 GitHub stars
Intelligent index suggestions including composite, partial, and covering indexes
Performance monitoring scripts for PostgreSQL, MySQL, and MongoDB
Query refactoring strategies to eliminate subqueries and optimize joins
Automated detection and fixing of N+1 query patterns in various ORMs

Casos de Uso

Optimizing application data layers to prevent database over-fetching
Designing efficient indexing strategies for new or existing schemas
Troubleshooting and fixing slow-running production queries