114 件 ・ 関連度順
概要と使いどころ
Profile construction data to understand characteristics, distributions, quality metrics, and patterns. Essential for data quality assessment and ETL planning.
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。
Profile and optimize application performance including load times, memory usage, and rendering. Use when debugging slow performance, memory leaks, or optimizing app speed.
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。
Native macOS/iOS app performance profiling via xctrace/Time Profiler and CLI-only analysis of Instruments traces. Use when asked to profile, attach, record, or analyze Instruments .trace files, find hotspots, or optimize native app performance without opening Instruments UI.
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。
Mobile app debugging for iOS, Android, cross-platform frameworks. Use for crashes, memory leaks, performance issues, network problems, or encountering Xcode instruments, Android Profiler, React Native debugger, native bridge errors.
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。
Profile and debug Hermes sessions from their JSONL transcripts. Find a session and its subagents, build a queryable event table, summarize the work as a hierarchical table of contents, break down wall time, inference, tools, tokens, and estimated cost per agent, identify errors and improvement opportunities, and export a shareable Perfetto trace. Use when a user wants to inspect what a Hermes session did, debug agent or subagent activity, understand session cost or latency, create a session timeline, generate a trace, or learn how to improve the next agent run.
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。
Workflow from differential expression results to functional enrichment analysis. Covers GO, KEGG, Reactome enrichment with clusterProfiler and visualization. Use when taking DE results to pathway enrichment.
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。
Visualize enrichment results using enrichplot package functions. Use when creating publication-quality figures from clusterProfiler results. Covers dotplot, barplot, cnetplot, emapplot, gseaplot2, ridgeplot, and treeplot.
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 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.
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。
SEO + Generative Engine Optimization (GEO) for 2026 — technical SEO, on-page optimization, structured data (JSON-LD, schema.org), Core Web Vitals, citability scoring (llms.txt, entity clarity), AI search engine optimization (ChatGPT, Gemini, Perplexity, Claude, Google AI Overviews), and brand authority building. Use when user asks to optimize a site for search engines, improve SEO, write SEO content, implement structured data, optimize for AI search/GEO, audit a page, or improve Google rankings. Do NOT use for content writing strategy (use content-marketing), performance optimization beyond Core Web Vitals (use performance-profiler), or paid ad strategy.
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。
Use when touching anything that runs per frame, adding entities/effects at scale, or answering a stutter/frame-drop report - frame budgets per target FPS, per-system split, the profiler per engine, before/after numbers, and threshold tests.
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。
System bottleneck identification, resource optimization, and performance analysis
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。
Use when turning perf, DTrace, pprof, or async-profiler stacks into an SVG flamegraph with the FlameGraph scripts, reading one, or diffing two profiles. Not for collecting data: use linux-perf.
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。
当用户希望使用 msOpProf（`msprof op` / `msprof op simulator`）对昇腾 AI 算子做上板或仿真性能调优、解释 `aic-metrics`/`trace.json`/`visualize_data.bin`、选择 device vs simulator 路径、排查 `--soc-version`/`--export`/`signal 6`/`Bad address`/热点图或流水图相关问题，或要求生成固定分析报告模板（算子基本信息 / 关键数据 TOP5 / 核心瓶颈 TOP5 / 优化建议 TOP5）时，使用本技能。它负责先判定模式、输入形态与芯片/能力边界，再给出正确命令、结果解读、固定报告输出与高频踩坑规避；不要把经验案例当成通用规则。
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。
MindStudio Profiler Analyze（msprof-analyze）是面向 AI 训练与推理场景的性能分析工具，基于采集得到的 profiling 数据进行统计、比对和诊断，帮助定位计算、通信、调度及集群场景下的性能瓶颈。
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。
DB格式Profiling数据场景化性能拆解：自动完成场景确认（框架/任务类型判定）、阶段拆解与单次执行边界拆解，并给出报告。Use when 需要对昇腾 Profiling DB 做阶段拆解、step 归因或生成性能报告, or when the user says "帮我按任务阶段拆解这个Profiling数据", "多层次性能耗时拆解", "分析这个 vllm/sglang/verl 数据，主要耗时都花费在哪里了"。
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。
运行程序采集 Profiling 数据，再用 msprof-analyze 分析TFLOPS 与 MFU。
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 profiling patterns including ProfilerMarkers, FrameTimingManager, memory profiling, and performance budgets.
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。
Runtime profiling with multi-metric measurement, instrumentation, and performance map generation
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。
Detects acquired antimicrobial-resistance determinants and chromosomal point-mutation resistance in bacterial assemblies using AMRFinderPlus, ResFinder 4.0 (acquired + PointFinder), CARD-RGI, abritAMR, staramr, and species-specific callers (TB-Profiler, Mykrobe). Harmonises cross-tool output via hAMRonization, contextualises determinants with mobile-genetic-element annotation (MOB-suite, PlasmidFinder, MobileElementFinder, ICEberg), predicts phenotype against EUCAST or CLSI breakpoints, and translates calls into WHO GLASS reporting categories. Use when screening clinical or surveillance isolates for AMR, distinguishing acquired vs intrinsic vs point-mutation resistance, calling rpoB / katG / pncA / gyrA / mgrB mutations, reconciling AMRFinderPlus vs RGI vs ResFinder disagreement, contextualising carbapenemases or mcr alleles on plasmids, predicting susceptibility from genotype against the WHO Mtb 2nd-edition catalogue, or building a hAMRonized multi-lab AMR surveillance pipeline.
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。
Assigns isolate identity at the right resolution for the question -- ANI/Mash species triage, 7-locus MLST historical comparability, cgMLST/wgMLST outbreak resolution (chewBBACA, BIGSdb, Ridom SeqSphere, EnteroBase HierCC), in-silico serotyping (SISTR/SeqSero2 Salmonella, SerotypeFinder E. coli, Kaptive Klebsiella, SeroBA pneumococcus, spa+SCCmec S. aureus), and lineage callers (TB-Profiler/Mykrobe barcode for MTBC, Pangolin + Nextclade for SARS-CoV-2, PopPUNK GPSC for S. pneumoniae). Use when typing bacterial isolates for surveillance or outbreak investigation, choosing between cgMLST allele distance and core-SNP distance for cluster definition, harmonising calls across schemas/database versions, assigning MTBC lineage with the Napier 90-SNP barcode, calling Salmonella serovar via SISTR with monophasic Typhimurium awareness, running Pangolin UShER mode with pangolin-data version pinning, or selecting a typing resolution to match the surveillance question.
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。
Extracts and assigns COSMIC v3.4 mutational signatures (86 SBS / 11 DBS / 18 ID / 21 CN / 16 SV) from somatic VCFs using SigProfilerSuite, MutationalPatterns, MuSiCal mvNMF, SigNet, or HRDetect. Use when characterizing DNA-damage etiology (BRCA1/2 HRD, MMR-D, POLE, APOBEC3A, UV, tobacco, aflatoxin, 5-FU/SBS17b, platinum, colibactin SBS88), routing PARP inhibitor decisions, or auditing de novo extraction vs refit choice for cohort size.
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。
Infers genetic super-population ancestry from a 23andMe/AncestryDNA file and computes ancestry-stratified odds ratios with an exploratory Ancestry Elevation Score (AES) showing where ancestry-specific GWAS effect sizes diverge from European reference estimates.
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。
Traces complete execution paths through DAG workflows. Records timing, inputs, outputs, and state transitions for all nodes. Activate on 'execution trace', 'trace execution', 'execution path', 'debug execution', 'execution log'. NOT for performance analysis (use dag-performance-profiler) or failure investigation (use dag-failure-analyzer).
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 DAG execution performance including latency, token usage, cost, and resource consumption. Identifies bottlenecks and optimization opportunities. Activate on 'performance profile', 'execution metrics', 'latency analysis', 'token usage', 'cost analysis'. NOT for execution tracing (use dag-execution-tracer) or failure analysis (use dag-failure-analyzer).
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。