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Found 298 Skills
Performance optimization patterns covering Core Web Vitals, React render optimization, lazy loading, image optimization, backend profiling, and LLM inference. Use when improving page speed, debugging slow renders, optimizing bundles, reducing image payload, profiling backend, or deploying LLMs efficiently.
heaptrack memory profiler skill for Linux. Use when tracking heap allocations, finding memory leaks, measuring peak heap usage, identifying allocation hotspots, or comparing allocation behaviour between runs. Activates on queries about heaptrack, heap profiling, memory allocation analysis, heaptrack_print, allocation hotspots, or memory leak detection with heaptrack.
Comprehensive guide and toolkit for diagnosing Rspack build issues. Quickly identify where crashes/errors occur, or perform detailed performance profiling to resolve bottlenecks. Use when the user encounters build failures, slow builds, or wants to optimize Rspack performance.
Optimized Rust build operations with timing, profiling, and workspace support
This skill should be used when working with CSV files to create interactive data visualizations, generate statistical plots, analyze data distributions, create dashboards, or perform automatic data profiling. It provides comprehensive tools for exploratory data analysis using Plotly for interactive visualizations.
Use when app feels slow, memory grows, battery drains, or diagnosing ANY performance issue. Covers memory leaks, profiling, Instruments workflows, retain cycles, performance 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.
Expert in system optimization, profiling, and scalability. Specializes in eBPF, Flamegraphs, and kernel-level tuning.
CUDA kernel development, debugging, and performance optimization for Claude Code. Use when writing, debugging, or optimizing CUDA code, GPU kernels, or parallel algorithms. Covers non-interactive profiling with nsys/ncu, debugging with cuda-gdb/compute-sanitizer, binary inspection with cuobjdump, and performance analysis workflows. Triggers on CUDA, GPU programming, kernel optimization, nsys, ncu, cuda-gdb, compute-sanitizer, PTX, GPU profiling, parallel 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.
1600+ lines of performance optimization mastery - profiling, rendering, memory, network, battery, APK size with production-ready code examples.
High-performance Rust optimization. Profiling, benchmarking, SIMD, memory optimization, and zero-copy techniques. Focuses on measurable improvements with evidence-based optimization.