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Found 13 Skills
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.
Analyses and optimises performance across frontend, backend and database interactions. Identifies bottlenecks and implements solutions to enhance speed and efficiency.
Profile-driven performance optimization with behavior proofs. Use when: optimize, slow, bottleneck, hotspot, profile, p95, latency, throughput, or algorithmic improvements.
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.
Optimize MATLAB code for better performance through vectorization, memory management, and profiling. Use when user requests optimization, mentions slow code, performance issues, speed improvements, or asks to make code faster or more efficient.
Identify CPU and memory bottlenecks in Python code using cProfile or memory_profiler. Use to optimize mission-critical Python services.
Hunt performance bottlenecks with swift precision. Stalk the slow paths, pinpoint the prey, streamline the code, catch the gains, and celebrate the win. Use when optimizing performance, profiling code, or hunting for speed.
This skill should be used when profiling code, optimizing bottlenecks, benchmarking, or when "performance", "profiling", "optimization", or "--perf" are mentioned.
Use this when the user asks about performance, slowness, optimization, or wants to make code more efficient. Focus on hot paths, unnecessary work, and algorithmic complexity.
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.
V8 JIT optimization patterns for writing high-performance JavaScript in Next.js server internals. Use when writing or reviewing hot-path code in app-render, stream-utils, routing, caching, or any per-request code path. Covers hidden classes / shapes, monomorphic call sites, inline caches, megamorphic deopt, closure allocation, array packing, and profiling with --trace-opt / --trace-deopt.
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.