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claude-codedevelopment

Claude-skill-registry code-profiler

Use when asked to profile Python code performance, identify bottlenecks, measure execution time, or analyze function call statistics.

install
source · Clone the upstream repo
git clone https://github.com/majiayu000/claude-skill-registry
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/code-profiler" ~/.claude/skills/majiayu000-claude-skill-registry-code-profiler && rm -rf "$T"
manifest: skills/data/code-profiler/SKILL.md
tags
#python#profiling#performance#code-analysis#optimization
source content

Code Profiler

Analyze Python code performance, identify bottlenecks, and optimize execution with comprehensive profiling tools.

Purpose

Performance analysis for:

  • Bottleneck identification
  • Function execution time measurement
  • Memory usage profiling
  • Call graph visualization
  • Optimization validation

Features

  • Time Profiling: Measure function execution times
  • Line-by-Line Analysis: Profile each line of code
  • Call Statistics: Function call counts and cumulative time
  • Memory Profiling: Track memory allocation and usage
  • Flamegraph Visualization: Visual call stack analysis
  • Comparison: Before/after optimization comparison

Quick Start

from code_profiler import CodeProfiler

# Profile function
profiler = CodeProfiler()
profiler.profile_function(my_function, args=(arg1, arg2))
profiler.print_stats(top=10)

# Profile script
profiler.profile_script('script.py')
profiler.export_report('profile_report.html')

CLI Usage

# Profile Python script
python code_profiler.py script.py

# Profile with line-by-line analysis
python code_profiler.py script.py --line-by-line

# Export HTML report
python code_profiler.py script.py --output report.html