「perf」の検索結果

Use when targeting Android/iOS — export and signing, permissions, plugins, in-app purchases, ads, app lifecycle, device features, and mobile 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。

Use when writing production-grade GDScript — performance idioms, metaprogramming, @tool lifecycle, async pitfalls, signal/Callable trade-offs, profiler-driven idioms, and common pitfalls

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。

Generate client-facing performance reports for Chinese wealth management clients. Adapted from the original client-report skill for A-share portfolios, Chinese regulatory requirements, and local reporting conventions. Triggers on "A股客户报告", "客户持仓报告", "client report China", "wealth report A-share", "客户业绩报告", or "portfolio report for [client]".

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。

Prepare for Chinese wealth management client meetings with performance summaries, talking points, and relationship context. Adapted from the original client-review skill for A-share portfolios and Chinese client conventions. Triggers on "A股客户回顾", "客户会议准备", "client review China", "wealth client review", "客户回访准备", or "prepare for client meeting [client]".

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。

Track and report on A-share portfolio company performance for China-focused private equity funds. Monitors KPIs, financials, and strategic milestones. Adapted from the original portfolio-monitoring skill for Chinese portfolio companies. Triggers on "A股投后管理", "基金投后监控", "portfolio monitoring China", "monitor portfolio company", "投后报告", or "portfolio company review".

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。

Universal Runtime best practices for PyTorch inference, Transformers models, and FastAPI serving. Covers device management, model loading, memory optimization, and performance tuning.

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。

RAG-specific best practices for LlamaIndex, ChromaDB, and Celery workers. Covers ingestion, retrieval, embeddings, and 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。

Debug a WRONG or imperfect render (not a hard error) by inspecting inputs and intermediate steps with run-to-node. Render one branch up to an output, preview-tap latents/masks/preprocessor maps, localize the first bad stage, then fix. Use when a final image/video completes but looks wrong, such as artifacts, wrong subject/pose/composition/color, blur, a ControlNet/IPAdapter/mask/LoRA not taking, or a two-stage refiner or upscale degrading the result. For errors/OOM/missing nodes use the troubleshooting skill.

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。

Deep single-page SEO analysis covering on-page elements, content quality, technical meta tags, schema, images, and performance. Use when user says "analyze this page", "check page SEO", "single URL", "check this page", "page analysis", or provides a single URL for review.

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。

Google SEO APIs: Search Console (Search Analytics, URL Inspection, Sitemaps), PageSpeed Insights v5, CrUX field data with 25-week history, Indexing API v3, and GA4 organic traffic. Provides real Google field data for Core Web Vitals, indexation status, search performance, and organic traffic trends. Use when user says "search console", "GSC", "PageSpeed", "CrUX", "field data", "indexing API", "GA4 organic", "URL inspection", "google api setup", "real CWV data", "impressions", "clicks", "CTR", "position data", "LCP", "INP", "CLS", "FCP", "TTFB", or "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。

Image optimization analysis for SEO and performance. Checks alt text, file sizes, formats, responsive images, lazy loading, CLS prevention, image SERP rankings (via DataForSEO), and image file optimization (WebP/AVIF conversion, IPTC/XMP metadata injection). Use when user says "image optimization", "alt text", "image SEO", "image size", "image audit", "optimize images", "image metadata", "image SERP", "convert to webp", or "image file optimize".

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。

AI-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score verification.

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。

MCP server optimization and transport layer enhancement for claude-flow v3. Implements connection pooling, load balancing, tool registry optimization, and performance monitoring for sub-100ms response times.

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。

Swift 6.2 InlineArray and Span types for zero-overhead memory access, fixed-size collections, and safe pointer alternatives. Use when optimizing performance-critical code paths.

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。

SwiftUI's actual mental model — view identity, lifetime, and dependencies (the Demystify canon), state ownership decision rules, Observation's per-property tracking, body-performance discipline, and the main-actor concurrency contract. Use when state resets mysteriously, views re-render too often, animations glitch between branches, choosing @State vs @Bindable vs plain property, or debugging "why did body 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。

SwiftUI layout beyond stacks — the Layout protocol (when custom layout beats GeometryReader), Grid vs lazy grids, custom containers with sections and container values, and lazy-stack/ScrollView performance rules (what breaks laziness, prefetch discipline, scroll APIs). Use when building custom layouts or containers, fixing lazy-stack jank or memory growth, or wiring programmatic/snapping scrolling.

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。

Diagnose SwiftUI performance issues including unnecessary re-renders, view identity problems, and slow body evaluations. Use when SwiftUI views are slow, janky, or re-rendering too often.

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。

Deep dive into SwiftData design patterns and best practices. Covers schema design, query patterns, repository pattern, and performance optimization. Use when designing data models or improving SwiftData usage.

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。

Swift language patterns and best practices including concurrency, performance, and modern idioms. Use for Swift language-level code review or architecture guidance.

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。

Survey any codebase as a senior advisor and produce prioritized, self-contained implementation plans for OTHER models/agents to execute. Strictly read-only on source code — never implements, fixes, or refactors anything itself. Use when asked to audit a codebase, find improvement opportunities (bugs, security, performance, test coverage, tech debt, migrations, DX), suggest features or where to take the project next (roadmap, product direction), or generate handoff plans for another agent to implement.

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。

Zenith performs gene set analysis on the result of differential expression using linear (mixed) modeling with dream by considering the correlation between gene expression traits. This package implements the camera method from the limma package proposed by Wu and Smyth (2012). Zenith is a simple extension of camera to be compatible with linear mixed models implemented in variancePartition::dream().

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。

Monocle performs differential expression and time-series analysis for single-cell expression experiments. It orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progre

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。

The analysis and visualization of alternative splicing (AS) events from RNA sequencing data remains challenging. SpliceWiz is a user-friendly and performance-optimized R package for AS analysis, by processing alignment BAM files to quantify read counts across splice junctions, IRFinder-based intron retention quantitation, and supports novel splicing event identification. We introduce a novel visualization for AS using normalized coverage, thereby allowing visualization of differential AS across

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。

mirTarRnaSeq R package can be used for interactive mRNA miRNA sequencing statistical analysis. This package utilizes expression or differential expression mRNA and miRNA sequencing results and performs interactive correlation and various GLMs (Regular GLM, Multivariate GLM, and Interaction GLMs ) analysis between mRNA and miRNA expriments. These experiments can be time point experiments, and or condition expriments.

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。