Profile and optimize Python application performance with cProfile, Pyinstrument, and Memray.
npx skills add https://github.com/stevenke1981/python_skills --skill py-perf
Please help me install this Agent Skill. Skill: py-perf Source: https://github.com/stevenke1981/python_skills/tree/main/py-perf Command: npx skills add https://github.com/stevenke1981/python_skills --skill py-perf
This Skill addresses performance regressions, high memory usage, and latency issues by providing a structured, data-driven approach to profiling and optimizing Python applications.
Use the py-perf skill to profile the execution of the main function in app.py and generate a flame graph to identify performance bottlenecks.
High-intent search queries and answers about installing and using this skill.
Diagnose Python performance bottlenecks by systematically profiling CPU and memory hotspots using tools like py-spy and memory_profiler. This Skill attaches a profiler to isolate latency issues in request-handling loops and identify resource-intensive code paths.
Benchmark Python code by establishing reproducible baselines and regression gates that validate optimizations. This Skill ensures performance testing provides measurable, stable improvements and prevents regressions across various Python environments.
Yes, it supports profiling I/O-heavy and CPU-bound workloads across various Python environments. The Skill performs systematic profiling and algorithmic analysis to measure and optimize application performance for these specific workload types.
The best way to profile a Python application with latency spikes is attaching a profiler to isolate the bottleneck in the request-handling loop. Use pyinstrument or line_profiler to generate flame graphs and verify the fix with a controlled benchmark.
Yes, you can use py-spy and pyinstrument to profile the execution of functions and generate flame graphs. This Skill leverages these dependencies to identify CPU performance bottlenecks and visualize resource consumption effectively.
High memory usage persists when optimizations lack measurable validation against reproducible baselines. Profile memory hotspots using memory_profiler to isolate intensive allocations, then verify the fix with controlled benchmarking to ensure stable resource consumption.
az9713Profile Python or Node services to identify bottlenecks and guide incremental performance improvements.
Jia-Hong-PengMeasure page load times, Core Web Vitals, and resource sizes for regression detection.
tronghieuProfile Python code with cProfile and py-spy to optimize CPU and memory usage.
gofhirBenchmark and profile Go code using the standard testing framework and pprof.
