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Found 35 Skills
Set up and run an autonomous experiment loop for any optimization target. Use when asked to start autoresearch or run experiments.
Identify CPU and memory bottlenecks in Python code using cProfile or memory_profiler. Use to optimize mission-critical Python services.
Analyze code performance, detect bottlenecks, suggest optimizations for algorithms, queries, and resource usage. Use when improving application performance or investigating slow code.
Use this when the user asks to refactor, clean up, optimize, or improve code quality.
Design, implement, optimize, and review SIMD code in .NET. USE FOR: vectorizing scalar loops with TensorPrimitives, Vector64/128/256/512, or platform hardware intrinsics; reviewing existing SIMD code, including Vector<T>, for contract equivalence, tail handling, memory safety, portability, fallbacks, and measured performance. DO NOT USE FOR: performance work unrelated to SIMD or vectorization.
Initialize evo for the current repository by exploring the codebase, proposing unexplored optimization dimensions, constructing the benchmark inside a baseline worktree, and running the first experiment. Use when the user invokes /evo:discover, mentions setting up evo, wants to instrument a codebase for autonomous optimization, or asks to start a new evo run on a project.
Split your code into smaller bundles to reduce initial load time and improve performance.
Automatically analyze performance issues when user mentions slow pages, performance problems, or optimization needs. Performs focused performance checks on specific code, queries, or components. Invoke when user says "this is slow", "performance issue", "optimize", or asks about speed.
Use when mapping code paths, entrypoints, and likely hot files before profiling.
Workflow for measuring and optimizing the ACIR circuit size of a constrained Noir program. Use when asked to optimize a Noir program's gate count or circuit size.
Whole-assembly call-graph leverage ranking and performance triage for libraries (experimental).