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Found 52 Skills
Measure, profile, optimize, and regression-test Rust performance across latency, throughput, CPU, allocations, memory, binary size, and compile time using representative workloads, Criterion, iai-callgrind, cargo-flamegraph, samply, DHAT, heap or pprof tools, cargo-bloat, cargo-llvm-lines, and build timings. Use when users report slow Rust code, high memory or CPU, large binaries, noisy benchmarks, tail-latency regressions, or need performance budgets and evidence-backed optimization.
Make code faster or find why it is slow, from measurements and not from guesses. Read this skill before you change code to make it faster or use less memory. Use it when the user reports slow code, high latency, or high memory use. Use it also to check a speed claim, or to compare the speed of two options. Use it to diagnose a slow or stuck process, a profile, or a trace file.
Refactor Pandas code to improve maintainability, readability, and performance. Identifies and fixes loops/.iterrows() that should be vectorized, overuse of .apply() where vectorized alternatives exist, chained indexing patterns, inplace=True usage, inefficient dtypes, missing method chaining opportunities, complex filters, merge operations without validation, and SettingWithCopyWarning patterns. Applies Pandas 2.0+ features including PyArrow backend, Copy-on-Write, vectorized operations, method chaining, .query()/.eval(), optimized dtypes, and pipeline patterns.
Process large datasets efficiently using chunk(), chunkById(), lazy(), and cursor() to reduce memory consumption and improve performance