Is it safe for database-heavy tests?

Yes, it specifically addresses common database bottlenecks like lock acquisition and provides strategies to replace slow operations with faster alternatives like row-level deletes.

What kind of common bottlenecks does it fix?

It targets frequent issues such as high-round password hashing in tests, expensive RSA key generation, inefficient database truncation, and incorrectly scoped fixtures.

Does it modify my production code?

No, all optimizations are strictly applied to test infrastructure, conftest files, or local test configurations to ensure production behavior remains unchanged.

How does this skill identify slow tests?

It uses native pytest duration tracking to find the slowest individual tests and phases (setup, call, teardown), then employs pyinstrument for deep CPU profiling of the execution path.

Do I need extra tools installed?

The skill utilizes pytest and recommends pyinstrument for detailed call trees. It can check for these dependencies and guide you through installation using uv or pip.

Pytest Profiling & Optimization

Name: Pytest Profiling & Optimization
Author: clemux

byclemux

0•

보안 및 테스팅

Diagnoses and speeds up slow Python test suites using systematic profiling and targeted optimization patterns.

Pytest Profiling & Optimization is a specialized workflow for Claude Code designed to eliminate guesswork when dealing with slow test suites. It provides a structured four-phase approach—Measure, Analyze, Optimize, and Report—to identify bottlenecks like expensive fixture setups, slow password hashing, or database lock contention. By integrating tools like pyinstrument and duration tracking, it helps developers achieve significant speedups in their CI/CD pipelines and local development cycles while maintaining test correctness and integrity.

주요 기능

01Pre-defined optimization patterns for common bottlenecks like bcrypt and SQL locks

02CPU call-tree profiling via integrated pyinstrument support

03Deep-dive fixture dependency tracing using --setup-show

04Comprehensive performance reporting with speedup metrics and delta tracking

050 GitHub stars

06Automated baseline measurement and identification of slowest test phases

사용 사례

01Identifying 'hidden' performance costs in shared test fixtures and setup logic

02Refactoring legacy test suites that have become sluggish over time

03Reducing CI/CD pipeline execution time for large Python projects

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add clemux/claude-code-plugins pytest-profiling

For use in Claude.ai and ChatGPT

Download Skill