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performance-profiling

Profile Python applications with cProfile and pstats to identify performance bottlenecks.

1|Updated May 5, 2026
Search Tags:#performance reporting#bottleneck detection#performance profiling#memory profiling#cProfile#python optimization#CPU profiling
One-click install
npx skills add https://github.com/kollaborai/kollab --skill performance-profiling-kollaborai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: performance-profiling
Source: https://github.com/kollaborai/kollab/tree/main/bundles/skills/performance-profiling
Command: npx skills add https://github.com/kollaborai/kollab --skill performance-profiling-kollaborai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill eliminates the guesswork of identifying application performance issues by providing a systematic, data-driven approach to profiling and bottleneck reporting, without requiring you to implement optimizations yourself.

Core Features & Use Cases

  • Comprehensive Profiling Coverage: Supports CPU, memory, I/O, network, and algorithmic performance analysis for Python terminal apps, web frameworks (Flask, FastAPI, Django), and data processing pipelines.
  • Tool Integration: Works with standard library profilers (cProfile, pstats) and optional advanced tools (line_profiler, memory_profiler, py-spy, snakeviz, tuna) for detailed insights.
  • Structured Reporting: Generates prioritized bottleneck reports with severity ratings, exact file/line locations, and actionable recommendations for coder agents to implement.
  • Use Case: If your Python data processing pipeline is running 3x slower than expected, use this Skill to profile its execution, identify the slowest functions and memory leaks, and get a clear list of fixes to apply.

Quick Start

Use the performance-profiling skill to analyze the execution time and memory usage of the main.py script in your current project and generate a full report of all identified performance bottlenecks.

Frequently Asked Questions about performance-profiling

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I find performance bottlenecks in a Python application?▼

To find performance bottlenecks in a Python application, you can use systematic profiling with standard library tools like cProfile and pstats to measure execution time and identify slow functions across CPU, memory, and I/O operations.

Can I profile memory usage in a Python data processing pipeline?▼

Yes, you can profile memory usage in a Python data processing pipeline using optional tools like memory_profiler to detect memory leaks and generate detailed reports on memory consumption alongside CPU performance analysis.

Does Python performance profiling work with web frameworks like FastAPI and Django?▼

Yes, Python performance profiling works with web frameworks like FastAPI, Flask, and Django, applying systematic execution analysis to identify CPU, network, and I/O bottlenecks specific to web workloads.

What is the best way to report performance bottlenecks without fixing them?▼

The best way to report performance bottlenecks without fixing them is to generate structured, data-driven reports with severity ratings, exact file and line locations, and actionable recommendations for coder agents to implement.

Do I need to install line_profiler and py-spy to profile Python code?▼

No, you do not need to install line_profiler and py-spy to profile Python code; standard library profilers like cProfile and pstats are sufficient, while those advanced tools are optional for deeper line-level or sampling insights.

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