How do I profile a specific function?

Create a CodeProfiler instance and call profile_function with the target function and its arguments, then view results with print_stats or export a report.

Can I profile an entire script instead of a single function?

Yes. Use profile_script('script.py') to profile the whole script and export a detailed HTML report if needed.

What profiling aspects are available?

Time profiling, line-by-line analysis, memory profiling, call statistics, and flamegraph visualization, with before/after comparison support.

code-profiler

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SKILL.md

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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

Source

git clone https://github.com/dkyazzentwatwa/chatgpt-skills/blob/main/code-profiler/SKILL.mdView on GitHub

Overview

The Code Profiler analyzes Python code performance to identify bottlenecks, measure execution time, and compare optimizations. It supports time profiling, line-by-line analysis, memory profiling, and flamegraph visualization to help optimize Python apps.

How This Skill Works

A CodeProfiler instance profiles either a function or a script, collecting timing data, per-line metrics, memory usage, and function call statistics. It then outputs stats and an HTML report or flamegraph for visual analysis and optimization validation.

When to Use It

When you need to identify bottlenecks in Python code
When you want to measure function execution time precisely
When you need memory usage profiling for a script or function
When visualizing the call graph with flamegraphs
When validating performance improvements with before/after comparisons

Quick Start

Step 1: from code_profiler import CodeProfiler; profiler = CodeProfiler()
Step 2: profiler.profile_function(my_function, args=(arg1, arg2))
Step 3: profiler.print_stats(top=10); profiler.export_report('profile_report.html')

Best Practices

Profile representative workloads that reflect real usage
Start with the top hotspots indicated by initial profiling
Use line-by-line profiling for narrow, critical sections judiciously
Always compare before/after results with an exportable report
Run multiple iterations to account for variability and cache effects

Example Use Cases

Profiling a data processing function in an ETL pipeline to locate slow transforms
Investigating a slow API endpoint by analyzing call counts and execution time
Tuning a machine learning training loop by reducing hot functions
Debugging memory leaks in a long-running script with memory profiling
Benchmarking performance changes after a code refactor using before/after reports

Frequently Asked Questions

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