3,190 件 ・ 関連度順
概要と使いどころ
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。
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。
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。
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。
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。
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。
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。
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。
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。
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。
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。
Expert knowledge for Azure Managed Lustre development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, and integrations & coding patterns. Use when mounting AML filesystems, linking Blob, using AKS CSI, setting quotas, or tuning Lustre performance, and other Azure Managed Lustre related development tasks. Not for Azure NetApp Files (use azure-netapp-files), Azure Virtual Machines (use azure-virtual-machines), Azure Virtual Network (use azure-virtual-network), Azure Virtual Network Manager (use azure-virtual-network-manager).
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 knowledge for Azure Nutanix development including decision making. Use when selecting NC2 on Azure regions, VM SKUs, and planning capacity, performance, availability, or cost, and other Azure Nutanix related development tasks. Not for Azure VMware Solution (use azure-vmware-solution), Azure Baremetal Infrastructure (use azure-baremetal-infrastructure), Azure Virtual Machines (use azure-virtual-machines), Azure Stack Edge (use azure-stack-edge).
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 knowledge for Azure Blob Storage development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when choosing Blob tiers, configuring NFS/SFTP or BlobFuse, securing with SAS/RBAC, tuning performance, or using SDK/CLI, and other Azure Blob Storage related development tasks. Not for Azure Files (use azure-files), Azure Queue Storage (use azure-queue-storage), Azure Table Storage (use azure-table-storage), Azure NetApp Files (use azure-netapp-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。
Improve the agent itself, or audit its recent performance - better skills, prompts, workflows, and config, plus a quality/reliability/memory-hygiene review of what it did and what failed
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。
Provides guidance for writing, optimizing, and benchmarking C++ CPU kernels with SIMD intrinsics (AVX2/AVX512) for the Hugging Face kernels ecosystem. Includes a two-phase workflow: Phase 1 correctness (generic → AVX2) and Phase 2 performance exploration (AVX512 with branching trial loop), runtime CPU dispatch, OpenMP threading, and brgemm integration for GEMM-heavy kernels.
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。
Provides guidance for writing and benchmarking optimized CUDA kernels for NVIDIA GPUs (H100, A100, T4) targeting HuggingFace diffusers and transformers libraries. Kernels must be kernel-builder/ABI3-compliant: no pybind11, no setup.py, TORCH_LIBRARY_EXPAND bindings only. Supports models like LTX-Video, Stable Diffusion, LLaMA, Mistral, and Qwen. Includes integration with HuggingFace Kernels Hub (get_kernel) for loading pre-compiled kernels. Includes benchmarking scripts to compare kernel performance against baseline implementations.
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 Amazon advertising strategy and campaign optimization. Sponsored Products, Sponsored Brands, Sponsored Display campaign management, budget allocation, ACoS optimization, and performance tracking. Use when the user asks about Amazon PPC, advertising strategy, campaign optimization, or Amazon ads management.
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。
Plan shoot-ready Amazon product photography, listing image sets, infographics, lifestyle scenes, and production briefs. Use when a seller asks for an Amazon photo shot list, creative direction, image-pack plan, photography budget, DIY-versus-done-for-you decision, or handoff to a photographer or image-generation workflow. Do not use for listing copy or image-only performance auditing.
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。
Amazon PPC campaign builder and optimizer for sellers. Two modes: (A) Build — design a complete campaign structure from scratch with keyword groupings, bid calculations, and negative keyword lists, (B) Optimize — audit existing campaigns using search term reports, identify keyword funnel opportunities, calculate bid adjustments, and generate a week-by-week action plan. Integrates with amazon-keyword-research for keyword input. No API key required. Use when: (1) setting up Amazon PPC campaigns for a new product, (2) auditing existing campaign performance and ACoS, (3) optimizing keyword bids and negative keywords, (4) building Auto/Manual/Exact campaign structures, (5) analyzing search term reports for opportunities, (6) calculating break-even ACoS and target ACoS, (7) scaling profitable campaigns to Sponsored Brands or Display.
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。