Can I revert the changes made by this skill?

Yes, the skill emphasizes keeping optimizations local and reversible to ensure code maintainability and easy rollback if necessary.

How does it verify performance gains?

The workflow requires reproducing the slow path and recording metrics both before and after changes to quantify the exact performance improvement.

Does this skill change my core algorithms?

No, the skill follows strict guardrails to avoid algorithmic changes unless specifically requested, focusing instead on implementation-level optimizations.

What profiling tools does this skill support?

It utilizes standard Python tools such as cProfile, pyinstrument, and line_profiler to measure execution time and identify code hotspots.

Python Performance Profiling

Name: Python Performance Profiling
Author: Silviase

bySilviase

0•

Analytics & Monitoring

Identifies performance bottlenecks in Python code through systematic profiling and applies targeted, measurable optimizations.

This skill provides a structured workflow for optimizing Python research code by identifying hotspots with profiling tools like cProfile or pyinstrument. It guides users through a repeatable process of measuring slow paths, applying minimal and safe fixes such as vectorization or caching, and verifying improvements with before-and-after metrics. By adhering to strict guardrails, it ensures that optimizations remain local, reversible, and data-driven without introducing unnecessary algorithmic complexity.

Key Features

01Targeted optimizations including vectorization and caching strategies

02Systematic bottleneck identification using cProfile, pyinstrument, or line_profiler

03Safe, local, and reversible code modifications to maintain stability

04Reproducible performance testing on representative datasets

050 GitHub stars

06Quantifiable before-and-after performance measurement and recording

Use Cases

01Reducing latency in backend Python services by identifying redundant I/O

02Profiling and caching expensive function calls to improve execution speed

03Optimizing slow data processing loops in Python research scripts

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add silviase/default-python-project refactoring-09-performance-profiling

For use in Claude.ai and ChatGPT

Key Features

01Targeted optimizations including vectorization and caching strategies

02Systematic bottleneck identification using cProfile, pyinstrument, or line_profiler

03Safe, local, and reversible code modifications to maintain stability

04Reproducible performance testing on representative datasets

050 GitHub stars

06Quantifiable before-and-after performance measurement and recording

Use Cases

01Reducing latency in backend Python services by identifying redundant I/O

02Profiling and caching expensive function calls to improve execution speed

03Optimizing slow data processing loops in Python research scripts

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add silviase/default-python-project refactoring-09-performance-profiling

For use in Claude.ai and ChatGPT