What tools does this skill recommend for monitoring?

It suggests a variety of tools including APM suites like New Relic and Datadog, profiling utilities like cProfile and py-spy, and load testing tools like k6.

Does it include guidance on caching?

Yes, it covers application-level caching (LRU), distributed caching with Redis, and HTTP-level caching using Cache-Control and ETags.

How does this skill help with slow database queries?

It provides patterns for identifying N+1 query problems, implementing proper indexing, and using EXPLAIN ANALYZE to optimize SQL execution plans.

Which programming languages are supported?

While many patterns are universal, the skill provides specific profiling and implementation examples for Python, Node.js, and SQL.

Can it help reduce application memory usage?

Yes, it includes specific techniques such as using __slots__ in Python classes and replacing lists with generators to minimize memory footprint during data processing.

Optimizing Performance

Name: Optimizing Performance
Author: drewdresser

bydrewdresser

数据库管理

Identifies and resolves performance bottlenecks across databases, application logic, and infrastructure using proven optimization patterns.

关于

This skill empowers Claude to act as a performance engineer, providing expert guidance on measuring and improving software efficiency across the entire stack. It covers a comprehensive range of optimizations, from solving the N+1 query problem and implementing distributed caching with Redis to leveraging asynchronous I/O and memory-efficient data structures. Whether you are debugging a slow API, reducing memory overhead, or optimizing database indices, this skill provides the profiling commands and implementation patterns necessary to build high-performance, scalable applications.

主要功能

Multi-level caching strategies using LRU cache, Redis, and HTTP headers.
Asynchronous processing patterns using Celery background jobs and async I/O.
0 GitHub stars
Systematic profiling and bottleneck identification for Python, Node.js, and SQL.
Database optimization including eager loading, indexing, and query refactoring.
Memory management techniques like generators, class slots, and weak references.

使用场景

Reducing API response times by identifying slow database queries and implementing caching.
Scaling application throughput by converting synchronous network calls to asynchronous tasks.
Refactoring memory-intensive data processing scripts to use generators for large datasets.

关于

主要功能

Multi-level caching strategies using LRU cache, Redis, and HTTP headers.
Asynchronous processing patterns using Celery background jobs and async I/O.
0 GitHub stars
Systematic profiling and bottleneck identification for Python, Node.js, and SQL.
Database optimization including eager loading, indexing, and query refactoring.
Memory management techniques like generators, class slots, and weak references.

使用场景

Reducing API response times by identifying slow database queries and implementing caching.
Scaling application throughput by converting synchronous network calls to asynchronous tasks.
Refactoring memory-intensive data processing scripts to use generators for large datasets.