performance-engineering

Profile Python applications using py-spy and scalene to collect CPU data, generate flamegraphs, and identify hot paths. This Skill structures the profiling session to locate slow paths and memory issues across your codebase efficiently.

Yes, you can use perf and flamegraph scripts to profile Rust services experiencing latency spikes under load. The Skill guides you through collecting CPU data, generating flamegraphs, and validating optimizations to rectify identified hotspots.

Analyze C/C++ memory leaks using heaptrack, valgrind, and massif to collect memory profiling data. The Skill provides structured guidance to locate memory issues, generate actionable reports with massif-visualizer, and rectify inefficient allocation paths.

Yes, the profiling workflow supports both Linux and macOS environments. It utilizes cross-platform tooling such as perf and dtruss, along with language-specific profilers, to ensure you can benchmark and debug applications across different operating systems.

Benchmark and compare performance using hyperfine to measure execution times before and after code changes. The Skill enables you to run a baseline profiling session, apply optimizations, and then validate improvements by comparing the updated results against the baseline.