Code Profiling

Name: Code Profiling
Author: Skills Guide Bot

VerifiedSafe

Identifies performance bottlenecks in code using cProfile, line_profiler, and timers. Analyzes cumulative time and call counts to suggest optimizations like vectorization and caching.

Sby Skills Guide Bot

DevelopmentIntermediate

206/2/2026

Claude Code

#profiling#performance#optimization#python#bottleneck

Our review

Profiles code execution speed to identify bottlenecks.

Strengths

Supports multiple profiling methods (time, cProfile, line_profiler)
Analyzes cumulative time and call counts
Provides optimization suggestions

Limitations

Requires local execution capability
Line profiler needs extra installation

When to use it

When a script or function is slow and you need to pinpoint the costly parts.

When not to use it

For production performance analysis or very simple scripts.

Security analysis

Safe

Quality score75/100

The skill only provides guidance on profiling Python code using standard tools. It does not instruct the AI to execute any commands or contain dangerous operations.

No concerns found

Examples

Profile with cProfile

Profile the execution of my script 'data_processing.py' and show the cumulative time sorted results.

Line-by-line profiling

Add @profile decorator to my function 'process_data' and run line profiler on 'app.py' to see per-line time.

Simple timer

Wrap this function in a timer to measure its execution time: [function code]

name: profile description: 指定したコードの実行速度をプロファイリングし、ボトルネックを特定する

Profile Code

This skill helps find performance bottlenecks in the code.

Instructions

Identify Target:
- Ask user for the script or function call to profile.
Run Profiler:
- Simple Timer: For quick checks, wrap code in time.perf_counter().
- cProfile: Run python -m cProfile -s cumulative <script_name.py>.
- Line Profiler: If detailed line-by-line analysis is needed, suggesting adding @profile decorator and running kernprof -l -v <script_name.py> (requires line_profiler installed).
Analyze Output:
- Look for functions with high cumtime (cumulative time).
- Look for functions with high call counts (ncalls).
Report:
- Summarize which parts of the code are consuming the most time.
- Suggest potential optimizations (vectorization, caching, algorithm change).