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performance-profiler

Estimate algorithmic complexity and parse profiler outputs to identify hotspots.

4|1|Updated Jan 26, 2026
Search Tags:#performance#caching#optimization#profiling#complexity#regression
One-click install
npx skills add https://github.com/wyattowalsh/agents --skill performance-profiler-wyattowalsh
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: performance-profiler
Source: https://github.com/wyattowalsh/agents/tree/main/skills/performance-profiler
Command: npx skills add https://github.com/wyattowalsh/agents --skill performance-profiler-wyattowalsh

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pypdf, pdfplumber, pdf2image, and includes scripts (resource) and references (resource) and data (resource) and evals (resource) components.

What problem does it solve?

This Skill helps identify and resolve performance bottlenecks in your code, leading to faster execution times and more efficient resource utilization.

Core Features & Use Cases

  • Complexity Analysis: Estimates the Big-O complexity of functions to flag potential algorithmic inefficiencies.
  • Profiler Output Parsing: Interprets output from tools like cProfile and py-spy to pinpoint hotspots.
  • Caching Strategy Design: Recommends optimal caching mechanisms based on access patterns.
  • Regression Risk Assessment: Evaluates the performance impact of code changes.
  • Use Case: You've noticed a specific API endpoint is slow. Use this Skill to analyze its complexity, interpret profiling data, and get actionable recommendations for optimization.

Quick Start

Use the performance-profiler skill to analyze the complexity of the file src/utils.py.

Frequently Asked Questions about performance-profiler

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

FAQPage Schema
How do I identify performance bottlenecks using profiler output from cProfile or py-spy?▼

Profiler output from tools like cProfile and py-spy is parsed to pinpoint execution hotspots and bottlenecks. By interpreting this textual data alongside static AST analysis, the tool provides actionable optimization guidance to resolve performance bottlenecks in your code.

How can I estimate the algorithmic complexity of my Python functions?▼

Algorithmic complexity is estimated by performing static analysis via AST parsing. This process evaluates your Python functions to flag potential algorithmic inefficiencies, estimating their Big-O complexity to help you identify and resolve performance bottlenecks proactively.

What is the best way to design caching strategies based on code access patterns?▼

Designing caching strategies involves analyzing your code to recommend optimal caching mechanisms based on access patterns. This approach directly addresses optimization guidance, ensuring efficient resource utilization and resolving performance bottlenecks in software execution.

How do I assess the performance regression risk of recent code changes?▼

Performance regression risk is assessed by evaluating the performance impact of code changes. By estimating algorithmic complexity and parsing profiler outputs, the tool identifies hotspots, bottlenecks, and regression risks in software execution to ensure optimizations are effective.

Can I analyze code performance and identify hotspots using AST parsing?▼

Yes, code performance is analyzed using static analysis via AST parsing. This mechanism estimates algorithmic complexity and, combined with interpreting textual data from profiling tools, identifies hotspots, bottlenecks, and regression risks in software execution.

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