About code-profiler
code-profiler is a skill for analyzing Python performance and locating bottlenecks. It measures execution time and memory use, with line-level analysis, call statistics, flamegraphs, and before-and-after comparisons.
Key features
- 01Function execution-time profiling
- 02Line-by-line analysis
- 03Call counts and cumulative time
- 04Memory allocation tracking
- 05Flamegraph visualization
- 06Before-and-after comparison
Use cases
- 01Finding slow functions in a Python script
- 02Inspecting memory usage
- 03Visualizing function call paths
- 04Checking an optimization's effect
Install
Copy the command and run it in a terminal.
$
npx skills add https:/After installing, type/code-profilerin Claude Code to confirm it loaded.
Bundled filesscripts/code_profiler.pyscripts/requirements.txt
Details
From the project READMECode 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
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