926건 · 관련도순
Claude API (platform.claude.com) の tool use / Agent Skills / MCP。 tool definition (JSON Schema), tool_use / tool_result, server tools（code execution, web search, web fetch, memory tool, tool search）, client toolset（computer use, browser use）, Agent Skills (SKILL.md), Skills API GA (/v1/skills), MCP connector, mcp_servers, mcp_tool_listing, MCP tunnels, Tunnels API。
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Claude Code (code.claude.com) の CLI 本体リファレンス。 CLAUDE.md memory, settings.json (All settings, settings precedence, managed-settings.json), env-vars, model config, keybindings, statusline, terminal config, cli-reference (-p / --continue / --resume), sessions, checkpointing, worktrees, remote control, channels, deep links, troubleshooting。
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Apple データ永続化・データフロー・ネットワーク・ユーティリティリファレンス。SwiftData / Core Data / Foundation / Combine。 @Model, ModelContainer, ModelContext, @Query, Predicate, FetchDescriptor, SectionedResults, ResultsObserver, NSManagedObject, NSFetchRequest, NSFetchedResultsController, NSPersistentContainer, NSPersistentCloudKitContainer, NSStagedMigrationManager, URLSession, JSONDecoder, JSONEncoder, Publisher, sink, Future, AsyncPublisher, @Published, AnyCancellable
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
NVIDIA DGX Spark User Guide リファレンス。GB10 Grace Blackwell Superchip 仕様、 first-boot / UEFI、ConnectX-7 Networking (旧 Spark Stacking)、複数 Spark 接続、 NVIDIA Sync Cluster Assistant、DGX OS / NGC / container runtime、 Release Notes (DGX OS 7.5.0)、known issues、PXE / cloud-init、fleet 運用、 apt / fwupdmgr、recovery、PSIRT、 NVIDIA Developer Forums 由来 troubleshooting、 build.nvidia.com/spark playbook (vLLM / vLLM cluster / TensorRT-LLM / Ollama / ComfyUI / NIM / NeMo / VLM fine-tuning / NVIDIA PAIR / NCCL)、Tailscale、VS Code。
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
App Store Connect API 4.5.1 (REST) アプリ配布リファレンス。JWT 認証、API キー作成・失効、 Apps, Builds, Bundle IDs, Bundle ID Capabilities, Certificates, Devices, Provisioning Profiles, Users, User Invitations, Merchant IDs, Pass Type IDs、TestFlight (Beta Groups / Testers)、 Sandbox Testers、App Review, Review Submissions, Customer Reviews, Custom Product Pages, Phased Release、In-App Purchases, Subscriptions, Subscription Groups, App Price Points, Promotional Offers, Win-back Offers, Sales Reports、App Asset Library (screenshots / app previews)、 Webhooks / Webhook Events / Deliveries、Xcode Cloud (Workflows, Build Runs, Artifacts)、 Alternative Distribution (EU マーケットプレイス / Web Distribution)、 Game Center (Achievements, Leaderboards, Challenges)、Analytics / 診断レポート。
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Apple グラフィックス。Metal, Core Animation, Core Graphics, Core Image, SpriteKit, SceneKit。 MTLDevice, MTKView, MTLBuffer, MTLTexture, MTLResidencySet, MTLTensor, Metal 4 (MTL4CommandQueue, MTL4CommandBuffer, MTL4CommandAllocator, MTL4ArgumentTable, MTL4RenderCommandEncoder, MTL4Compiler), CALayer, CABasicAnimation, CADisplayLink, CAMetalDisplayLink, CAMetalDrawable, CGContext, CGPath, CGImage, CIFilter, CIContext, CIImage, CIRAWFilter, CIWarpKernel, CIBlendKernel, SKScene, SKSpriteNode, SKAction, SCNNode, SCNGeometry, SCNMaterial。
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Driver.js (プロダクトツアー・要素ハイライト) リファレンス。 driver(), highlight, tour steps, popover, overlay, hints (beacon), onboarding checklist, DestroyReason, advanceOnClick, waitForElement, skipMissingElement, theming, configuration, コールバック, 1.5〜1.10 変更点。
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
EditorConfig (エディタ設定統一フォーマット) リファレンス。 .editorconfig ファイル、glob パターン、root、 indent_style / indent_size / tab_width、end_of_line, charset、 trim_trailing_whitespace, insert_final_newline, max_line_length, spelling_language。
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Apple オンデバイス機械学習フレームワークリファレンス。 Core AI (.aimodel) / Core ML / Create ML / Vision / Natural Language / Speech / Media Intelligence / Music Understanding。 AIModel, InferenceFunction, MLModel, MLModelConfiguration, MLMultiArray, MLTensor, MLComputeUnits, MLImageClassifier, MLTextClassifier, MLDataTable, VNCoreMLRequest, VNImageRequestHandler, VNRecognizeTextRequest, RecognizeTextRequest, NLTagger, NLEmbedding, NLLanguageRecognizer, SpeechAnalyzer, SpeechTranscriber, SFSpeechRecognizer, SFSpeechAudioBufferRecognitionRequest, SFTranscription, FaceGroupAnalyzer, MusicUnderstandingSession。
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
visionOS / iOS 空間コンピューティング。RealityKit、ARKit、visionOS SwiftUI、USDKit、Spatial Preview。 Entity, ModelEntity, AnchorEntity, Component, System, RealityView, MeshResource, Material, EntityAction, MeshDeformerComponent, GaussianSplatComponent, ComputeGraphComponent, ComputeNodeGraph, PhysicsJointsComponent, ForceEffectComponent, ClothBodyComponent, NavigationComponent, NavigationMeshComponent, NavigationController, USDStage, SpatialPreviewSession, ImmersiveSpace, ImmersionStyle, WindowGroup, VolumetricWindowStyle, Model3D, ARKitSession, ARSession, ARWorldTrackingConfiguration, WorldTrackingProvider, HandTrackingProvider, ImageTrackingProvider, AccessoryTrackingProvider, CameraFrameProvider, ObjectTrackingProvider, RoomTrackingProvider, SceneReconstructionProvider。
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Detect syntenic blocks and structural rearrangements between genomes using MCScanX (Wang 2012), JCVI/MCScan (Tang 2008 Python), GENESPACE (Lovell 2022) for orthology-anchored riparian visualization, SyRI for structural variation, AnchorWave for sequence-level synteny, i-ADHoRe 3.0 for highly diverged species, SynNet for synteny networks, and ntSynt for multi-genome macrosynteny. Use when identifying collinear gene blocks across species, distinguishing macrosynteny from microsynteny, detecting inversions/translocations/duplications, anchoring orthology in WGD lineages, producing publication riparian plots, computing synteny block age via Ks (cross-references whole-genome-duplication), or running synteny-aware ortholog inference in polyploids.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Visualizes ChIP-seq data using deepTools (computeMatrix, plotHeatmap, plotProfile, bamCoverage, bamCompare), pyGenomeTracks (modern INI-driven track plots), Gviz (R browser-style), EnrichedHeatmap (ComplexHeatmap-based), ChIPseeker tag heatmaps, and IGV batch screenshots. Handles bigWig normalization choices (CPM, BPM, RPGC, spike-in scaled), bamCompare operations (log2 ratio, subtract, SES), k-means clustering of heatmaps for biological subgrouping, and spike-in-scaled tracks for global-shift experiments. Use when generating publication-quality ChIP-seq signal heatmaps, profile plots, genome-browser tracks, or comparing samples visually.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Refactor Spring Boot and Java code to improve maintainability, readability, and adherence to enterprise best practices. This skill transforms messy Spring Boot applications into clean, well-structured solutions following SOLID principles and Spring Boot 3.x conventions. It addresses fat controllers, improper transaction boundaries, field injection anti-patterns, and scattered configuration. Leverages Java 21+ features including record patterns, pattern matching for switch, virtual threads, and sequenced collections.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build container-based Foundry Agents with Azure AI Projects SDK (ImageBasedHostedAgentDefinition). Use when creating hosted agents with custom container images in Azure AI Foundry.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Autonomous rule adherence checker. Scans the codebase for rule violations, fixes the highest-impact ones in an isolated worktree, runs full validation, creates a PR. Uses memory to track progress across runs.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Systematically review pull requests, feature implementations, and code changes to ensure quality, maintainability, security, and adherence to best practices. Use when reviewing code before merging, conducting peer reviews, performing self-reviews, auditing code quality, checking for security vulnerabilities, ensuring consistent coding standards, verifying test coverage, assessing performance implications, evaluating architectural decisions, or providing constructive feedback to improve team code quality.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Review code for AEM Edge Delivery Services projects. Use at the end of development (before PR) for self-review, or to review pull requests. Validates code quality, performance, accessibility, and adherence to EDS best practices.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build container-based Foundry Agents with Azure AI Projects SDK (ImageBasedHostedAgentDefinition). Use when creating hosted agents with custom container images in Azure AI Foundry.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Detect syntenic blocks and structural rearrangements between genomes using MCScanX (Wang 2012), JCVI/MCScan (Tang 2008 Python), GENESPACE (Lovell 2022) for orthology-anchored riparian visualization, SyRI for structural variation, AnchorWave for sequence-level synteny, i-ADHoRe 3.0 for highly diverged species, SynNet for synteny networks, and ntSynt for multi-genome macrosynteny. Use when identifying collinear gene blocks across species, distinguishing macrosynteny from microsynteny, detecting inversions/translocations/duplications, anchoring orthology in WGD lineages, producing publication riparian plots, computing synteny block age via Ks (cross-references whole-genome-duplication), or running synteny-aware ortholog inference in polyploids.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Visualizes ChIP-seq data using deepTools (computeMatrix, plotHeatmap, plotProfile, bamCoverage, bamCompare), pyGenomeTracks (modern INI-driven track plots), Gviz (R browser-style), EnrichedHeatmap (ComplexHeatmap-based), ChIPseeker tag heatmaps, and IGV batch screenshots. Handles bigWig normalization choices (CPM, BPM, RPGC, spike-in scaled), bamCompare operations (log2 ratio, subtract, SES), k-means clustering of heatmaps for biological subgrouping, and spike-in-scaled tracks for global-shift experiments. Use when generating publication-quality ChIP-seq signal heatmaps, profile plots, genome-browser tracks, or comparing samples visually.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Architects the encryption, codec/transport, and premium-feature layer of a group video-calling product, on top of an already-chosen SFU topology. Covers E2EE patterns (Zoom leader-fan-out, Signal pairwise-fan-out, MLS/TreeKEM as in Discord's DAVE, Telegram's ledger consensus), the SFrame (RFC 9605) + WebRTC Insertable Streams mechanism letting an SFU relay encrypted frames it can't read, codec/transport choice (AV1/VP9 SVC vs. simulcast, Opus, congestion control, Media-over-QUIC, WHIP/WHEP), and which "premium" features (virtual backgrounds, live captions, recording, breakout rooms, spatial audio) survive E2EE vs. structurally can't. Use when adding E2EE to an SFU-based call, choosing a 2026 codec/SVC strategy, or deciding which features to disable under E2EE mode. NOT for call topology/platform choice (see webrtc-adhoc-video-chat, which this composes with). NOT for one-way broadcast (see managed-video-streaming-pipeline). NOT for the DM/group-chat text backend (see realtime-messaging-backend-architecture).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Library of accessibility personas representing people with various disabilities, impairments, and situational limitations. Use this skill when users ask about disability types, accessibility personas, user needs for specific conditions, how people with disabilities use technology, assistive technology users, or designing for accessibility. Triggers on requests about blindness, deafness, cognitive disabilities, motor impairments, low vision, screen readers, sign language, autism, ADHD, temporary disabilities, or any question about "how would a person with X use this".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Apply 37signals/DHH Rails conventions when writing Ruby on Rails code. Use when building Rails applications, reviewing Rails code, or making architectural decisions. Covers various aspects of Rails application architecture, design and dependencies.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build container-based Foundry Agents with Azure AI Projects SDK (ImageBasedHostedAgentDefinition). Use when creating hosted agents with custom container images in Azure AI Foundry.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.