386 件 ・ 関連度順
概要と使いどころ
Suede-owned prospecting and qualification discipline. Use when defining an ICP, sourcing and enriching a bounded lead list, finding early adopters or design partners, scoring account fit, or documenting disqualification evidence. NOT FOR: sending outreach (use suede-cold-email), changing CRM routing (use suede-revops), or profiling competitors instead of prospects (use suede-competitor-profiling).
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Suede-owned customer-research discipline for interview design, transcript and ticket synthesis, review and forum mining, quote banks, jobs, and evidence-backed personas. Use when discovering or synthesizing what a defined customer segment actually says, does, needs, and resists. NOT FOR: competitor-only profiling (use suede-competitor-profiling), writing final marketing copy (use suede-copy), or deciding product priorities without product evidence (use suede-product-marketing).
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Performance profiling and optimization for web apps — Core Web Vitals (LCP, INP, CLS), Lighthouse audits, bundle analysis, backend profiling (CPU, memory, DB queries), N+1 detection, caching strategies (Redis, CDN, HTTP), and performance budgets. Use when user asks to improve performance, run Lighthouse audit, profile a Node.js app, optimize Core Web Vitals, reduce bundle size, or investigate slow response times. Do NOT use for database schema optimization (use db-sculptor), Docker image optimization (use docker), or CDN configuration.
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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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Use when profiling CUDA with Nsight Systems or Nsight Compute, reading roofline and occupancy metrics, or annotating phases with NVTX. Not for correctness: use cuda-debugging.
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Analyze Ascend NPU schedule, operator dispatch, operator launch, and Host Bound profiling issues in Ascend profiling data. Use when need to diagnose device Free time, framework/operator dispatch latency, launch latency, PYTORCH_API/CANN_API launch gaps, aclrtSynchronizeStream stalls, task queue behavior, CPU scheduling interference, GC/lock pauses, CPU affinity, or schedule-side optimization actions.
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 principles. Measurement, analysis, and optimization techniques.
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。
Unity 6 performance profiling and optimization guide. Use when profiling, optimizing frame rate, reducing memory usage, debugging performance bottlenecks, or using the Profiler, Memory Profiler, or Frame Debugger. Based on Unity 6.3 LTS documentation.
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 principles. Measurement, analysis, and optimization techniques.
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。
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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三方框架特性落地（原 framework-feature-enablement ＋ framework-extension-dev 合并）： 一个入口信号「框架侧特性没落地」，内部分两分支——**分支 A 框架已有 → 使能与验证** （计数契约 + 三层证据 + 使能异常回修）；**分支 B 框架缺失 → 补齐开发** （注入点 + 代码组织/注册机制 + 合入姿势：平台注册优先 / 上游 PR / fork 钉版 / monkey 备选）。 分支 A 触发词：量化/稀疏/缓存开关没生效、融合算子没命中、使能异常或精度不符、 "起 vllm 服务""模型在框架里跑不通""把 mindiesd 接到其他框架"。 分支 B 触发词：框架没 comm-stream 掩盖、框架没缓存消费者、框架不支持某 collective/mask、 feature_* NotImplemented、要给框架补一条结构性能力。 near-miss：纯安装/权重 → env-install；纯采集/分析 → profiling-collect / profiling-analyze； 纯选档（该不该开量化、开哪一档）→ dit-perf-opt； mindiesd 仓内 pattern / 算子 / 图下发开发 → dev-workflow + pattern-dev / operator-dev / aclgraph-dev。 由 model-auto-optimization 的 S1（融合接入）/S4（有损使能）/S5（训练感知）与 §0 缺口补齐策略路由， 亦由 dev-workflow 的框架接入/验证场景指引加载。
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环境安装与准备：把部署环境从零安装就绪——mindiesd 编译安装（本地昇腾直装 / SSH 推远端容器 / Docker 镜像直装）与三方推理框架全栈安装（vLLM-Omni 源码构建、DiffSynth-Engine 部署、 LightX2V editable 部署），并负责模型权重确认与下载（下载前先确认远端是否已存在）。不含特性使能与验证 （framework-integration）与 profiling（profiling-collect）；SSH 工具由 remote-access 提供。 当用户需要安装 MindIE-SD、源码构建/直装 vLLM-Omni 或 LightX2V（editable + PLATFORM=ascend_npu）、 或确认/下载模型权重时使用此技能； 即使用户只提到"把代码推到服务器""在容器里装 vllm 全栈""准备 lightx2v 调优环境"而未说昇腾， 只要上下文涉及环境安装与准备都应触发。由 model-auto-optimization S0 与 dev-workflow 部署阶段指引加载。
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Profiles RNA-binding protein targets without antibody or UV crosslinking using STAMP (APOBEC1-RBP fusion, C-to-U editing), scSTAMP (single-cell), TRIBE/HyperTRIBE (ADAR-RBP, A-to-I editing), DART-seq (APOBEC1-YTH for m6A), or Bullseye/SAILOR edit-site detection pipelines. Use when antibody is unavailable or specificity is doubtful, when single-cell RBP profiling is needed (scSTAMP), or when in vivo RBP profiling without UV is preferred.
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NVIDIA RAG Blueprint performance-tuning guidance for profiling retrieval stacks, comparing bottlenecks, and validating latency or throughput improvements.
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Data Quality Specialist IA — Expert en qualité des données (profiling, cleaning, déduplication, validation de schéma, Great Expectations)
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Guide for training outputs, metrics logging, logtree reports, tracing/profiling, and debugging training runs. Use when the user asks about training logs, metrics, debugging, tracing, profiling, timing, Gantt charts, or understanding training output files.
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 principles. Measurement, analysis, and optimization techniques.
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。
Comprehensive ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) profiling for drug candidates. Integrates ADMET-AI predictions, SwissADME drug-likeness, PubChemTox experimental toxicity, ChEMBL clinical data, Lipinski rule-of-five, and CYP interaction data. Use for drug-likeness assessment, BBB penetration, bioavailability, hepatotoxicity prediction, ADME/PK profiling, or screening compound libraries before lab testing.
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Designs and analyzes stable-isotope-resolved metabolomics (SIRM / isotope tracing / fluxomics) experiments that measure metabolic ACTIVITY via 13C/15N/2H tracers, distinct from steady-state pool profiling. Covers tracer choice, isotopologue vs isotopomer, mass-isotopomer distributions (MID), fractional enrichment, the mandatory natural-abundance + tracer-purity correction (IsoCor, AccuCor), and the metabolic/isotopic steady-state vs non-stationary (INST-MFA) distinction. Use when feeding a labeled tracer and interpreting labeling patterns, correcting raw isotopologue intensities, computing or plotting an MID, or deciding tracing vs abundance profiling. For absolute pool concentration and MRM mechanics see metabolomics/targeted-analysis; for constraint-based genome-scale flux (FBA, not empirical tracing) see systems-biology/flux-balance-analysis; for feature detection see metabolomics/xcms-preprocessing; for pathway enrichment that ignores the pool-vs-flux caveat see metabolomics/pathway-mapping.
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。
Profiles RNA-binding protein targets without antibody or UV crosslinking using STAMP (APOBEC1-RBP fusion, C-to-U editing), scSTAMP (single-cell), TRIBE/HyperTRIBE (ADAR-RBP, A-to-I editing), DART-seq (APOBEC1-YTH for m6A), or Bullseye/SAILOR edit-site detection pipelines. Use when antibody is unavailable or specificity is doubtful, when single-cell RBP profiling is needed (scSTAMP), or when in vivo RBP profiling without UV is preferred.
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 application performance through code splitting, lazy loading, caching strategies, bundle size reduction, render optimization, and profiling. Use when improving page load times, reducing bundle sizes, optimizing React rendering, implementing code splitting, configuring caching strategies, lazy loading components and routes, optimizing images and assets, profiling performance bottlenecks, implementing virtual scrolling for large lists, or improving Core Web Vitals and Lighthouse scores.
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。
Profiles RNA-binding protein targets without antibody or UV crosslinking using STAMP (APOBEC1-RBP fusion, C-to-U editing), scSTAMP (single-cell), TRIBE/HyperTRIBE (ADAR-RBP, A-to-I editing), DART-seq (APOBEC1-YTH for m6A), or Bullseye/SAILOR edit-site detection pipelines. Use when antibody is unavailable or specificity is doubtful, when single-cell RBP profiling is needed (scSTAMP), or when in vivo RBP profiling without UV is preferred.
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 optimization and measurement for .NET applications. Navigation skill covering Span, ArrayPool, memory management, benchmarking, profiling, Native AOT, and optimization patterns. For building high-performance applications. Keywords: performance, optimization, span, arraypool, benchmarking, profiling, memory, gc, aot, native-aot
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Identifies API latency hotspots and bottlenecks with profiling tools, slow endpoint detection, suspected causes, and fix roadmap. Use for "latency profiling", "performance bottlenecks", "slow APIs", or "backend 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。