Profile Python code with cProfile or line_profiler to identify bottlenecks.
npx skills add https://github.com/tatsuki-washimi/gwexpy --skill profile-tatsuki-washimi
Please help me install this Agent Skill. Skill: profile Source: https://github.com/tatsuki-washimi/gwexpy/tree/main/.agent/skills/profile Command: npx skills add https://github.com/tatsuki-washimi/gwexpy --skill profile-tatsuki-washimi
Profiling code to identify performance bottlenecks and optimize execution time.
Run the profiler on your Python script to generate a report and identify bottlenecks.
High-intent search queries and answers about installing and using this skill.
Profile Python code using time.perf_counter, cProfile, or line_profiler to measure execution speed and identify bottlenecks. The profiler automatically highlights hot paths and function-level hotspots, generating a report with recommended optimizations like vectorization or caching.
Use cProfile for sampling function-level execution speed across large codebases, and line_profiler for line-by-line performance tuning within specific hotspots. Quick timing measurements for individual scripts or modules use time.perf_counter to identify bottlenecks fast.
Yes, you can profile execution speed across small to large Python codebases and complex workflows. The profiler measures performance bottlenecks and generates a report with hotspot functions and recommended optimizations, scaling from individual scripts to full modules.
After profiling identifies bottleneck hotspots, optimize Python functions by applying recommended improvements such as vectorization, caching, or algorithm enhancements. The generated profiling report provides actionable recommendations based on the measured execution speed results.
You do not need line_profiler exclusively, as time.perf_counter and cProfile also measure Python script execution time. The profiler requires interfaces for these three tools to identify bottlenecks and generate recommendations for performance tuning.
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