「performance profiling」の検索結果

Multi-Agent Optimization Toolkit workflow skill. Use this skill when the user needs Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Multi-Agent Optimization Toolkit workflow skill. Use this skill when the user needs Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Diagnoses slow services and regressions via profiling and tail-latency math. Use when diagnosing slow services, profiling hot paths, or judging whether an optimization is real.

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Add Rcpp or RcppArmadillo integration to an R package for high-performance C++ code. Covers setup, writing C++ functions, RcppExports generation, testing compiled code, and debugging. Use when an R function is too slow and profiling confirms a bottleneck, when you need to interface with existing C/C++ libraries, or when implementing algorithms (loops, recursion, linear algebra) that benefit from compiled code.

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Profiling strategy for audio CPU issues — xruns, spikes, and buffer underruns. Use when the user reports CPU overload, audio glitches under load, or performance regressions. Trigger on phrases like "I'm getting xruns", "my plugin causes CPU spikes", "dropouts under high voice count", "audio thread is too slow", "how do I profile my DSP". References audio-numerics-review for denormal slowdown and audio-dsp-review for lock contention.

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Application performance specialist for profiling, optimization, and scaling strategies

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Runtime profiling with multi-metric measurement, instrumentation, and performance map generation

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多模态模型自动优化流程总入口（编排层）：把一个三方框架托管的扩散模型从环境准备到无损/有损 优化自动跑完。任务判定后按 S0 环境准备、S1 kernel 融合优化（原 S1/S2 合并）、S3 并行通信、 S4 有损优化、S5 训练感知（预留，少步蒸馏 + SLA/QAT）、闭环复验路由到能力技能（env-install / framework-integration / profiling-collect / profiling-analyze / performance-optimization / dit-parallel-opt / dummy-run 等），检查各阶段产物与验收并回填经验槽位。 当用户需要对具体模型做框架接入、无损/有损加速、并行调优或确认收益时使用本入口；即使用户只提 模型名加"优化/加速/跑通/采profile"而未说框架或阶段，也应由本入口判定路由。仓库代码开发类任务 请走 dev-workflow。流程执行/验收按 workflows/optimization-flow.md，run-state + stage_gate 门禁。

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Application performance monitoring — transactions, profiling, and finding slow code — works with any APM platform.

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Performance audit — bottleneck profiling, N+1 query detection, hot-path analysis; explicit request only, not part of regular feature work.

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Investigate latency, freezes, memory growth, hitches, dropped frames, or perceived slowness with reproduction and instrumentation before optimizing.

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Expert-level guidance for Flutter production applications: architecture decisions (Clean/Layered/Hexagonal/Feature-First), advanced state management (BLoC/Riverpod/GetX), performance optimization (eliminating jank, memory profiling, reducing rebuild overhead), code review with design pattern enforcement, scalability assessments, native platform integration (MethodChannel/EventChannel/FFI), complex UI challenges (custom render objects, slivers), testing strategies (unit/widget/integration/golden), CI/CD pipeline architecture, build optimization, and accessibility compliance. Triggers: 'architecture decision', 'design pattern', 'performance issue', 'code review', 'production problem', 'scaling concerns', 'best practices', 'technical debt', 'refactoring strategy'. Essential for complex enterprise apps, critical production debugging, architectural refactors, or when choosing between competing technical approaches.

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Use when working with Bazel — bazel build system management. Covers BUILD file analysis, target querying, dependency graphs, remote execution, caching configuration, and build performance profiling. Use when managing Bazel workspaces, debugging build failures, querying targets, or optimizing build performance.

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Activate this skill when BenchmarkDotNet (BDN) is involved in the task — creating, running, configuring, or reviewing BDN benchmarks. Also activate when microbenchmarking .NET code would be useful and BenchmarkDotNet is the likely tool. Consider activating when answering a .NET performance question requires measurement and BenchmarkDotNet may be needed. Covers microbenchmark design, BDN configuration and project setup, how to run BDN microbenchmarks efficiently and effectively, and using BDN for side-by-side performance comparisons. Do NOT use for profiling/tracing .NET code (dotnet-trace, PerfView), production telemetry, or load/stress testing (Crank, k6).

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Audit and improve SwiftUI runtime performance from code review and architecture. Use for requests to diagnose slow rendering, janky scrolling, high CPU/memory usage, excessive view updates, or layout thrash in SwiftUI apps, and to provide guidance for user-run Instruments profiling when code review alone is insufficient.

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Memory management, profiling, optimization techniques, and Instruments usage for high-performance iOS apps

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Audit SwiftUI runtime performance from code first. Use when diagnosing slow rendering, janky scrolling, expensive updates, or profiling needs.

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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.

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Visualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance with TensorBoard - Google's ML visualization toolkit

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Guides Rust performance optimization. Use when profiling, benchmarking, reducing allocations, improving cache locality, choosing between rayon/async/threads, or applying SIMD/parallelism.

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Audit and improve SwiftUI runtime performance from code review and architecture. Use for requests to diagnose slow rendering, janky scrolling, high CPU/memory usage, excessive view updates, or layout thrash in SwiftUI apps, and to provide guidance for user-run Instruments profiling when code review alone is insufficient.

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Test and debug browser UIs using Chrome DevTools MCP — DOM inspection, console errors, network requests, performance traces, and visual verification. Load when building or debugging anything that renders in a browser, verifying a UI fix, profiling Core Web Vitals in a real page, or the user asks for browser testing with DevTools. Requires chrome-devtools MCP configured. Not for backend-only or CLI work. Pairs with frontend-design and performance-optimization.

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Expert-level browser automation, debugging, and performance analysis using Chrome DevTools MCP. Use for interacting with web pages, capturing screenshots, analyzing network traffic, and profiling performance.

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Optimize a measured performance problem when the user names a latency/load/Core Web Vitals/resource target or current profiling evidence identifies a bottleneck; do not trigger from generic implementation, review, or an unsupported suspicion that code may be slow.

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