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Found 8 Skills
Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.
Python performance optimization patterns using profiling, algorithmic improvements, and acceleration techniques. Use when optimizing slow Python code, reducing memory usage, or improving application throughput and latency.
Identify CPU and memory bottlenecks in Python code using cProfile or memory_profiler. Use to optimize mission-critical Python services.
Optimizes Python library performance through profiling (cProfile, PyInstrument), memory analysis (memray, tracemalloc), benchmarking (pytest-benchmark), and optimization strategies. Use when analyzing performance bottlenecks, finding memory leaks, or setting up performance regression testing.
Use this skill for mathematical code verification. Use when reviewing math-heavy code, verifying algorithm correctness, checking numerical stability, aligning with mathematical standards. Do not use when general algorithm review - use architecture-review. DO NOT use when: performance optimization - use parseltongue:python-performance.
Consult this skill for async Python patterns and concurrency. Use when building async APIs, concurrent systems, I/O-bound applications, implementing rate limiting, async context managers. Do not use when CPU-bound optimization - use python-performance instead. DO NOT use when: testing async code - use python-testing async module.
Expert-level performance optimization, profiling, benchmarking, and tuning
Performance profiling and bottleneck detection for Node.js, Python, and browser apps