Can this help with raw SQL queries?

Yes, it includes detection logic for raw SQL anti-patterns, such as identifying 'SELECT *' usage and queries missing appropriate LIMIT or indexing strategies.

Which ORMs does this skill support?

The skill provides specific patterns and fix examples for Prisma, Drizzle, TypeORM, and Sequelize, and can be easily customized for other libraries.

How does this skill detect N+1 queries?

It utilizes specialized grep patterns to locate database queries executed within loops—such as forEach, map, and for...of—identifying where batching or joins should be used instead.

Does it provide automated fixing?

It provides a scanning script to find issues and 'Fix Pattern' templates that Claude can use to refactor your code safely and efficiently.

Database Performance & Anti-Patterns

Name: Database Performance & Anti-Patterns
Author: JaveedIshaq

byJaveedIshaq

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Database Management

Identifies and remediates common database performance bottlenecks like N+1 queries, unbounded fetches, and connection pool issues.

About

This Claude Code skill empowers developers to proactively detect and fix inefficient database patterns that degrade application performance. It provides specialized detection logic for N+1 queries, sequential awaits, and missing pagination across popular ORMs like Prisma, Drizzle, and TypeORM. By offering both automated grep-based scanning scripts and concrete refactoring templates, it helps teams optimize their data access layer, reduce database load, and ensure scalable connection management.

Key Features

Connection pool exhaustion prevention and transaction optimization
Identification of sequential queries for Promise.all parallelization
Support for major ORMs including Prisma, Drizzle, and TypeORM
1 GitHub stars
N+1 query detection and batching remediation patterns
Automated scanning for unbounded fetches and missing LIMIT clauses

Use Cases

Refactoring data access layers to handle high-traffic scaling
Optimizing cloud database costs by reducing redundant query volume
Auditing legacy codebases for latent performance bottlenecks

About

Key Features

Connection pool exhaustion prevention and transaction optimization
Identification of sequential queries for Promise.all parallelization
Support for major ORMs including Prisma, Drizzle, and TypeORM
1 GitHub stars
N+1 query detection and batching remediation patterns
Automated scanning for unbounded fetches and missing LIMIT clauses

Use Cases

Refactoring data access layers to handle high-traffic scaling
Optimizing cloud database costs by reducing redundant query volume
Auditing legacy codebases for latent performance bottlenecks