performance-optimization

Apply systematic performance optimization techniques for Python and Rust code: estimation + profiling, API/bulk design, algorithmic wins, cache-friendly memory layout, fewer allocations, fast paths, caching, and compiler-friendly hot loops. Use for performance code reviews, refactors, and profiling-driven optimizations. Keywords: performance, latency, throughput, cache, allocation, memory layout, PyO3, msgspec, tokio, async, pprof, py-spy, perf.

Key Features

• Comprehensive skill evaluation and performance tracking
• Community-driven ratings and reviews
• Easy integration with Claude Code
• Regular updates and maintenance

Quick Start

TopRank Skills install dj-bolt/performance-optimization