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Found 9 Skills
Profile CPU usage to identify hot spots and bottlenecks. Optimize code paths consuming most CPU time for better performance and resource efficiency.
Profile or debug CPU usage and memory allocation in deployed Cloudflare Workers and Durable Objects. Then use this information to optimize your code.
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.
JVM performance tuning - GC optimization, profiling, memory analysis, benchmarking
Use this skill when profiling application performance, debugging memory leaks, optimizing latency, benchmarking code, or reducing resource consumption. Triggers on CPU profiling, memory profiling, flame graphs, garbage collection tuning, load testing, P99 latency, throughput optimization, bundle size reduction, and any task requiring performance analysis or optimization.
Interactive debugger for Deno/TypeScript applications using the V8 Inspector Protocol. This skill should be used when investigating issues in Deno applications, including memory leaks, performance bottlenecks, race conditions, crashes, or any runtime behavior that requires step-by-step debugging, heap analysis, or CPU profiling. Provides CDP client tools, heap/CPU analyzers, and investigation tracking.
Use when profiling CPU/memory hot paths, generating flame graphs, or capturing JFR/perf evidence.
Profile a running web application's CPU performance using Cursor's built-in browser profiler. Captures call stacks, identifies slow functions, and suggests optimizations. Use when a page feels slow or janky.
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.