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Found 246 Skills
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 performance decisions for iOS/tvOS: when to optimize vs premature optimization, profiling tool selection, SwiftUI view identity trade-offs, and memory management strategies. Use when debugging performance issues, optimizing slow screens, or reducing memory usage. Trigger keywords: performance, Instruments, Time Profiler, Allocations, memory leak, view identity, lazy loading, @StateObject, retain cycle, image caching, faulting, batch operations
Apply systematic performance optimization techniques when writing or reviewing code. Use when optimizing hot paths, reducing latency, improving throughput, fixing performance regressions, or when the user mentions performance, optimization, speed, latency, throughput, profiling, or benchmarking.
Correlates performance targets with actual profiling results. Identifies bottlenecks and validates against non-functional requirements.
Profile datasets to understand schema, quality, and characteristics. Use when analyzing data files (CSV, JSON, Parquet), discovering dataset properties, assessing data quality, or when user mentions data profiling, schema detection, data analysis, or quality metrics. Provides basic and intermediate profiling including distributions, uniqueness, and pattern detection.
Use for Luau performance work focused on profiling hotspots, allocation-aware code structure, table and iteration costs, builtin and function-call fast paths, compiler/runtime optimization behavior, and environment constraints that change execution speed.
Activate when a project needs competitive analysis, audience profiling, or positioning gaps before design begins.
R3F performance optimization—LOD (Level of Detail), frustum culling, instancing strategies, draw call reduction, frame budgets, lazy loading, and profiling tools. Use when optimizing render performance, handling large scenes, or debugging frame rate issues.
Advanced sub-skill for PyTorch focused on deep research and production engineering. Covers custom Autograd functions, module hooks, advanced initialization, Distributed Data Parallel (DDP), and performance profiling.
React render performance patterns including React Compiler integration, memoization strategies, TanStack Virtual, and DevTools profiling. Use when debugging slow renders, optimizing large lists, or reducing unnecessary re-renders.
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.
Linux perf profiler skill for CPU performance analysis. Use when collecting sampling profiles with perf record, generating perf report, measuring hardware counters (cache misses, branch mispredicts, IPC), identifying hot functions, or feeding perf data into flamegraph tools. Activates on queries about perf, Linux performance counters, PMU events, off-CPU profiling, perf stat, perf annotate, or sampling-based profiling on Linux.