「performance profiling」の検索結果

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.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.

日本語の概要は準備中です。原文の説明を表示しています。

Master systematic debugging techniques, profiling tools, and root cause analysis to efficiently track down bugs across any codebase or technology stack. Use when investigating bugs, performance issues, or unexpected behavior.

日本語の概要は準備中です。原文の説明を表示しています。

Performance profiling — find bottlenecks, measure against budgets, produce ranked optimization recommendations.

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Capture and interpret iOS Simulator ETTrace profiles. Use when profiling launch or runtime latency, comparing traces, or finding CPU-heavy stacks.

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Diagnose demonstrated CPU, command latency, allocation or IO regressions in an owned process with controlled measurements and language-appropriate profilers. Route benchmark effectiveness, score growth and trajectory efficiency to loopx-benchmark; profiling does not qualify a budget or grant execution authority.

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Use only for MSBuild failure investigation, build-performance analysis, or review of existing build XML/extension contracts. A plan to convert a working legacy/non-SDK project to SDK style or migrate packages.config is a project-system migration, NOT a build-file review: do not activate, even to check scope. Framework/API upgrades are also excluded. Activate for advisory Import/Exists hook and NuGet packed-import questions even without a checkout or failed build; .csproj/.fsproj/.vbproj/.props/.targets conditions; custom-target inputs/outputs; .binlog capture/replay; failing dotnet build/restore/pack/publish or MSBuild-based test builds; slow evaluation, compilation, copying, or no-change builds; and controlled timing comparisons. Exclude SDK installation alone, C# refactoring, runtime profiling, assertions after successful builds, and non-MSBuild builds.

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Inspect and profile React Native component trees from agent-device. Use for React Native performance, profiling, props, state, hooks, render causes, slow components, excessive rerenders, or questions like why a component rerendered.

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Evidence-gated workflow for MoE performance optimization in Megatron Bridge. Covers measurement contracts, the Three Walls framework, parallel folding, profiling, matched A/B tuning, and final validation.

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Golang data structures — slices (internals, capacity growth, preallocation, slices package), maps (internals, hash buckets, maps package), arrays, container/list/heap/ring, strings.Builder vs bytes.Buffer, generic collections, pointers (unsafe.Pointer, weak.Pointer), and copy semantics. Use when choosing or optimizing Go data structures, implementing generic containers, using container/ packages, unsafe or weak pointers, or questioning slice/map internals. Not for applying optimization patterns once profiling has identified a bottleneck (→ See `samber/cc-skills-golang@golang-performance` skill).

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Guides performance investigation, benchmarking, and optimization of the Grida Canvas web editor (TypeScript reducer, Immer, React hooks). Use when profiling reducer dispatch cost, diagnosing slow interactions (drag, resize, color change), writing or running editor benchmarks, instrumenting with PerfObserver, or optimizing the JS-side state management pipeline.

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Debug and profile Three.js browser games: blank canvases, render and runtime bugs, asset and audio loading, animation, resize, mobile input, plus performance profiling of draw calls, triangles, textures, memory, shader and post-processing cost, and bundle size.

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Session-scoped hook lifecycle management with enable/disable/status controls, execution profiling, and color-coded performance alerts.

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Master systematic debugging techniques, profiling tools, and root cause analysis to efficiently track down bugs across any codebase or technology stack. Use when investigating bugs, performance issues, or unexpected behavior.

日本語の概要は準備中です。原文の説明を表示しています。

Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.

日本語の概要は準備中です。原文の説明を表示しています。

Integrate Core ML models in iOS apps for on-device machine learning inference. Covers model loading (.mlmodel, .mlpackage, .mlmodelc), predictions with auto-generated classes and MLFeatureProvider, compute unit configuration (CPU, GPU, Neural Engine), MLTensor, VNCoreMLRequest, MLComputePlan, multi-model pipelines, and deployment strategies. Use when loading Core ML models, making predictions, configuring compute units, or profiling model performance.

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Generate, verify, refine, and accelerate C/C++ or CUDA code from MATLAB with MATLAB Coder, Embedded Coder, GPU Coder, or MATLAB Test. Also covers writing codegen-ready MATLAB code: language constraints, coder.* directives, and optimization patterns. Triggers on: codegen, MEX, deploy MATLAB as C/C++, GPU Coder, coder.screener, coder.config, coder.gpuConfig, coder.typeof, coder.runTest, matlabtest.coder.TestCase, SIL, embedded config, no dynamic memory, EnableMexProfiling, coder.timeit, coder.perfCompare, %#codegen, writing codegen-ready MATLAB, code generation readiness, coder.varsize, coder.unroll, coder.noImplicitExpansionInFunction, coder.ceval, coder.inline, coder.extrinsic, coder.const, coder.classSignature, class codegen limitations, temporal types codegen, DMA-off, stack-only, host-target InstructionSetExtensions, SIMDAcceleration, OptimizeReductions, host SIMD tuning, host OpenMP, codegen performance.

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Iterative measurement-driven improvement loop. Measure, profile, mutate, re-measure, commit. Works for performance, bundle size, complexity, test coverage — anything quantifiable. Use when the user wants to systematically improve a metric through repeated cycles of profiling and targeted changes.

日本語の概要は準備中です。原文の説明を表示しています。

Performance profiling and analysis

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Browser-based QA combining Chrome MCP control with Python analysis tools. Use when performing browser QA testing, visual regression tracking, WCAG accessibility auditing, performance profiling, or health-scoring web applications.

日本語の概要は準備中です。原文の説明を表示しています。

GDPR Legitimate Interest Assessment (LIA) — Guided assessment for Art. 6(1)(f) GDPR using the EDPB three-step test. Based on EDPB Guidelines 1/2024, EDPB Opinion 28/2024 (AI models), the OSS Case Digest on Legitimate Interest (McIntyre 2026), CNIL AI guidance (June 2025), ICO guidance, and key CJEU case law. Use when: (1) user needs to assess whether legitimate interest is appropriate, (2) user asks about Art. 6(1)(f), "Interessenabwägung", "berechtigtes Interesse", "balancing test", or "LIA", (3) user wants to document a legitimate interest assessment, (4) user mentions the three-step test, (5) user evaluates necessity or proportionality under Art. 6(1)(f), (6) user discusses direct marketing, fraud prevention, IT security, employee monitoring, AI training, web scraping, or credit checks in context of legal basis, (7) user handles a right to object (Art. 21), (8) user asks about children's data, profiling, or cross-border data sharing under legitimate interest, (9) user compares legitimate interest against other legal bases. Do NOT use for: consent assessments (Art. 6(1)(a)), contract performance (Art. 6(1)(b)), DPIA workflows, or pure ePrivacy/cookie compliance questions — though the skill does address the ePrivacy/GDPR overlap where relevant.

日本語の概要は準備中です。原文の説明を表示しています。

Master systematic debugging techniques, profiling tools, and root cause analysis to efficiently track down bugs across any codebase or technology stack. Use when investigating bugs, performance issues, or unexpected behavior.

日本語の概要は準備中です。原文の説明を表示しています。