Results for “profiler”

Adapt torch_npu.profiler interface for AI frameworks and training/inference code on Ascend NPU. Use this skill whenever users need to integrate profiling, add performance data collection, or instrument PyTorch code with torch_npu.profiler on Huawei Ascend NPU platforms. Triggers on: "torch_npu profiler", "NPU profiling", "Ascend profiler", "profiler adaptation", "profile training", "profile inference", "add profiler to framework", "性能采集", "profiling 数据", "昇腾 profiler", and any request to add performance monitoring to Ascend NPU code.

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Analyze 2D fluorescence microscopy images to detect nuclei and measure their counts, intensity, and area. Run repeatable CellProfiler batches, inspect nucleus outlines, and review measurement tables with recorded pipeline settings and quality checks.

• Count nuclei across microscopy image batches
• Measure fluorescence intensity and area for each nucleus
• Inspect nucleus outlines for segmentation errors

面向 Ascend PyTorch Profiler / msprof DB（如 ascend_pytorch_profiler*.db、msprof_*.db）的 SQL 分析技能。将自然语言问题（算子耗时、通信、下发、调度、schema/table 查询）转为安全可执行 SQL，并按需从官方文档提取表结构详情。

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Measure denoising latency in SGLang diffusion models and identify slow operations with profiler traces. Compare performance and output quality on the same GPUs, check existing optimizations, and gather evidence before starting low-level kernel work.

• Measuring diffusion denoising latency
• Ranking slow operations from profiler traces
• Checking whether existing optimizations address a bottleneck

Prerequisites and connection details for attaching to a live Python process with PyFlightProfiler. Read this skill first before using any other PyFlightProfiler command (watch, trace, stack, perf, etc.).

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当用户提供 MindStudio profiler 采集的性能数据（框架 profiler、msprof 命令行）时，对数据完整性、采集状态及关键配置进行校验，确保后续分析工具能正常运行。

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Analyze LLM serving traces to identify GPU time usage, opportunities to run operations concurrently, and known patterns for combining operations. Produce three consistent tables from saved traces or live captures on supported servers.

• Identify GPU operations that dominate inference time
• Compare input processing and token generation
• Trace GPU operations back to Python code

Find and fix game performance problems methodically — measure with the engine profiler first, reason about the frame-time budget, locate the CPU-vs-GPU bottleneck, then apply the right fix: object pooling, draw-call batching, fewer allocations/GC spikes, and asset budgets. Engine- neutral method that pairs with each engine's profiler. Use when the user mentions performance, optimize, low/dropping FPS, frame drops, stutter, lag, profiler, frame budget, draw calls, batching, garbage collection/GC spikes, object pooling, or "the game runs slow".

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Expert debugging and profiling for leaks, GPU/Visual Profiler, headless CI QA, orphan nodes, thread-safe logs, and custom Debugger monitors — not print/breakpoint tutorials. Trigger on OBJECT_ORPHAN_NODE_COUNT, ObjectDB growth, Visual Profiler GPU spikes, flaky headless exits, or remote device consoles. Keywords: orphan nodes, Performance.get_monitor, Visual Profiler, Time.get_ticks_usec, EditorDebuggerPlugin, headless QA, push_error, backtrace.

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Expert debugging and profiling for leaks, GPU/Visual Profiler, headless CI QA, orphan nodes, thread-safe logs, and custom Debugger monitors — not print/breakpoint tutorials. Trigger on OBJECT_ORPHAN_NODE_COUNT, ObjectDB growth, Visual Profiler GPU spikes, flaky headless exits, or remote device consoles. Keywords: orphan nodes, Performance.get_monitor, Visual Profiler, Time.get_ticks_usec, EditorDebuggerPlugin, headless QA, push_error, backtrace.

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Use the open-source free `Asynkron.Profiler` dotnet tool for CLI-first CPU, allocation, exception, contention, and heap profiling of .NET commands or existing trace artifacts. USE FOR: Asynkron.Profiler setup; automation-friendly profiling output; CPU, allocation, exception, contention, and heap investigation. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

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Start shotgun metagenomics workflows for mixed-community DNA reads in FASTQ files. Check read quality, remove host sequences, and profile organisms and biological functions using supported tools and databases, producing tables and visual summaries when results are available.

• Checking FASTQ quality and analysis readiness
• Removing host-derived sequences before profiling
• Summarizing microbial composition across samples

Analyze cryptocurrency wallets using balances, profit and loss, transaction history, and address labels. Profile several wallets together, trace connections across transaction counterparties, compare shared tokens and contacts, and export selected results as CSV.

• Review a wallet's balances and profit or loss
• Trace transaction counterparties across multiple steps
• Compare tokens and counterparties shared by two wallets

通过结构化访谈和写作样本分析，构建创作者个人声音画像（语气/用词/节奏/风格），确保后续内容风格一致。 当用户说"声音画像""我的写作风格""风格一致""建立人设语气""voice/tone""我的表达习惯""统一文风"时使用。 和 skill-audience-profiler 的区别：本 SKILL 建创作者自己的声音画像；audience-profiler 建目标受众/粉丝画像。

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Use when a Unity 6.3 game is slow or stutters: low fps, frame spikes, hitches when entering areas or on first effects, GC spikes, too many draw calls or SetPass calls, CPU vs GPU bound questions, Profiler, Profile Analyzer, Memory Profiler, Frame Debugger or Project Auditor work, GPU Resident Drawer or SRP Batcher setup, LOD and Mesh LOD, shader warm-up and variants, memory or VRAM budgets, IL2CPP and build size, or proving a fix with numbers from a player.

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基于可复现基线和 profiler 证据分析性能问题。用于用户要求定位慢接口、慢任务、慢查询、内存或网络瓶颈，制定优化方案，或验证优化收益时；区分实测结论与静态推断，不虚构耗时和提升比例，生产环境操作需明确授权。

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Expert blueprint for performance profiling and optimization (frame drops, memory leaks, draw calls) using Godot Profiler, object pooling, visibility culling, and bottleneck identification. Use when diagnosing lag, optimizing for target FPS, or reducing memory usage. Keywords profiling, Godot Profiler, bottleneck, object pooling, VisibleOnScreenNotifier, draw calls, MultiMesh.

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Expert blueprint for performance profiling and optimization (frame drops, memory leaks, draw calls) using Godot Profiler, object pooling, visibility culling, and bottleneck identification. Use when diagnosing lag, optimizing for target FPS, or reducing memory usage. Keywords profiling, Godot Profiler, bottleneck, object pooling, VisibleOnScreenNotifier, draw calls, MultiMesh.

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Use when the user is investigating QML / Qt Quick performance — both vague complaints ("the UI feels laggy", "this is slow", "frames are dropping", "the app stutters") and explicit asks to profile, find hotspots, or optimize bindings, signals, or rendering. Runs qmlprofiler on a 2D QML application, parses the .qtd trace, and analyzes hotspots against the source with frame-time, memory, and pixmap-cache summaries. Does NOT cover Qt Quick 3D.

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Use when you need to set up Java application profiling to detect and measure performance issues — including trusted preinstalled async-profiler v4.x setup, problem-driven profiling (CPU, memory, threading, GC, I/O), interactive profiling scripts, JFR integration with Java 25 (JEP 518, JEP 520), or collecting profiling data with flamegraphs and JFR recordings. This should trigger for requests such as Improve the code with profiling; Apply Profiling; Refactor the code with profiling; Add profiling support; Collect JFR or async-profiler data for Java performance. Part of Plinth Toolkit

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Set up the Graphsignal Profiler for inference workloads — vLLM, SGLang, PyTorch, and dstack services. Use when the user wants GPU profiling, tracing, or monitoring for inference, asks about `graphsignal-run` or `graphsignal.watch()`, or asks about CUPTI / Prometheus / OTLP setup.

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Inspect live Python class instances in a running process — find all instances of a class, examine their attributes/state, or invoke methods on them. Also supports forcing GC. Use this when you need to probe object-level runtime state beyond what watch/getglobal can provide.

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Trace execution time of Python method invocations in a live process with call stack visualization. Use this to see a hierarchical call tree showing which sub-calls are slow and where time is spent within a function.

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Hot-reload a Python function implementation from the latest file content without restarting the process. Use this to apply code fixes live after identifying a bug with watch/trace.

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