'modal' 검색 결과

Navigation in React Native — both React Navigation v7 (classical, modular) and Expo Router (file-based, Expo 49+). Stack, tab, drawer navigators, deep linking, typed navigation, modal presentation, authentication flow patterns. Use this skill to: - Pick navigation lib based on what's installed (@react-navigation/* vs expo-router). - Configure typed routes with ParamList types or Expo Router's generated types. - Set up deep linking (URL schemes, Universal Links, App Links). - Build authentication flow with conditional navigators. - Use modal presentation correctly. Do NOT use this skill for: - Project structure (see rn-conventions). - Platform-specific differences (see rn-platform-specific). - State / storage (see rn-state-and-storage). - Testing navigation (see rn-testing).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Guide a person through healing modalities including energy work (reiki, chakra balancing), herbal remedies, basic first aid stabilization, and holistic techniques (breathwork, visualization, body scan). AI coaches the practitioner through assessment triage, modality selection, energetic connection, remedy preparation, and integration. Use when a person describes a physical ailment or injury, reports energetic imbalance (fatigue, emotional stagnation), wants coaching through a holistic breathwork or visualization session, or needs post-meditation integration with directed healing attention.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Fix Playwright automation failures against code-server (VS Code in browser). Use when: (1) trust dialog blocks all clicks — "monaco-dialog-modal-block intercepts pointer events", (2) button:has-text() finds wrong buttons behind modal dialog, (3) keyboard shortcuts don't work — Meta vs Control inconsistency, (4) VS Code extension activity bar icon not found by aria-label, (5) panels show placeholder text despite extension being "Connected". Covers trust dialog dismissal, keyboard shortcut hybrid mode, extension panel selectors, and database connection verification for Grafema extension.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image, editing, composition, refinement). Use when working with audio/video files, analyzing images or screenshots, processing PDF documents, extracting structured data from media, creating images from text prompts, or implementing multimodal AI features. Supports multiple models (Gemini 2.5/2.0) with context windows up to 2M tokens.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert prompt engineering for Seedance 2.0. Use when the user wants to generate a video with multimodal assets (images, videos, audio) and needs the best possible prompt.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when the user asks about using Gemini in an enterprise environment or explicitly mentions Vertex AI, Google Cloud, or Agent Platform. Guides the usage of the Gemini API on Agent Platform with the Google Gen AI SDK. Covers SDK usage (Python, JS/TS, Go, Java, C#), capabilities like multimodal inputs, tools, media generation, caching, batch prediction, and Live API.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image, editing, composition, refinement). Use when working with audio/video files, analyzing images or screenshots, processing PDF documents, extracting structured data from media, creating images from text prompts, or implementing multimodal AI features. Supports multiple models (Gemini 2.5/2.0) with context windows up to 2M tokens.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Process images, video, audio, PDFs with Gemini API. Generate images (Nano Banana 2), videos (Veo 3), speech (MiniMax TTS), music (MiniMax). Convert documents to Markdown. Multi-provider fallback (Gemini → MiniMax → OpenRouter). Activate when task references media files, asks to analyze/describe/transcribe/extract/generate/convert, or involves non-text binary files.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert-level mechanical vibrations covering free and forced vibration, damping, resonance, modal analysis, vibration isolation, and rotating machinery.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Framework de pré-treinamento visão-linguagem que conecta codificadores de imagem congelados e LLMs. Use quando você precisar de legendagem de imagens, resposta a perguntas visuais, recuperação imagem-texto ou chat multimodal com desempenho zero-shot de última geração.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Orientação especializada para fine-tuning de LLMs com LLaMA-Factory - WebUI sem código, 100+ modelos, QLoRA 2/3/4/5/6/8-bit, suporte multimodal

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Plataforma GPU serverless em nuvem para executar workloads de ML. Use quando você precisar de acesso GPU sob demanda sem gerenciamento de infraestrutura, fazendo deploy de modelos de ML como APIs, ou executando jobs em batch com auto-scaling.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Framework de dados para construir aplicações LLM com RAG. Especializado em ingestão de documentos (300+ conectores), indexação e consultas. Inclui índices vetoriais, mecanismos de consulta, agentes e suporte multimodal. Use para perguntas sobre documentos, chatbots, recuperação de conhecimento ou construção de pipelines RAG. Ideal para aplicações LLM centradas em dados.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

吉里吉里Z 上の Elements ベース汎用ダイアログ/画面 UI (cycfi/elements + elements_modal) の作り方リファレンス。TJS で JSON / Dictionary 定義のダイアログを作る・出す・イベントを受ける・複数画面フロー(navigator)や常駐メニューを組む・入力/フォーカス/モーダルを制御する・Agent で動作検証する、といった場面で使う。基本的なダイアログ画面の作り方(最小例・レイアウト JSON スキーマ・ウィジェット一覧)から、モーダル/非モーダル/独立ウィンドウ/常駐フロー、複数インスタンス/z-order、DrawDevice 登録タイミング等のハマりどころまで網羅。Win32 ネイティブの WIN32Dialog(win32dialog プラグイン)とは別物。TJS2 言語仕様は skill `tjs2`、本体クラス API は `krkrz`、REPL/Agent 駆動は `krkrz-repl` を参照。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Azure AI Content Understanding SDK for Python. Use for multimodal content extraction from documents, images, audio, and video. Triggers: "azure-ai-contentunderstanding", "ContentUnderstandingClient", "multimodal analysis", "document extraction", "video analysis", "audio transcription".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

When the user wants to create or optimize popups, modals, overlays, slide-ins, or banners for conversion purposes. Also use when the user mentions "exit intent," "popup conversions," "modal optimization," "lead capture popup," "email popup," "announcement banner," "overlay," "collect emails with a popup," "exit popup," "scroll trigger," "sticky bar," or "notification bar." Use this for any overlay or interrupt-style conversion element. For forms outside of popups, see cro. For general page conversion optimization, see cro.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

When the user wants to create or optimize in-app paywalls, upgrade screens, upsell modals, or feature gates. Also use when the user mentions "paywall," "upgrade screen," "upgrade modal," "upsell," "feature gate," "convert free to paid," "freemium conversion," "trial expiration screen," "limit reached screen," "plan upgrade prompt," "in-app pricing," "free users won't upgrade," "trial to paid conversion," or "how do I get users to pay." Use this for any in-product moment where you're asking users to upgrade. Distinct from public pricing pages (see cro) — this focuses on in-product upgrade moments where the user has already experienced value. For pricing decisions, see pricing.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

既存デザインシステムコンポーネント（Button / Input / Modal / Card 等）を再実装していないかを検出する。Figma→コード実装時に既存コンポーネントを無視した実装を防ぐ。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use this skill alongside figma-use when the task involves translating an application page, view, or multi-section layout into Figma. Triggers: 'write to Figma', 'create in Figma from code', 'push page to Figma', 'take this app/page and build it in Figma', 'create a screen', 'build a landing page in Figma', 'update the Figma screen to match code', 'convert this modal/dialog/drawer/panel to Figma'. This is the preferred workflow skill whenever the user wants to build or update a full page, modal, dialog, drawer, sidebar, panel, or any composed multi-section view in Figma from code or a description. Discovers design system components, variables, and styles from Code Connect files, existing screens, and library search, then imports them and assembles views incrementally section-by-section using design system tokens instead of hardcoded values.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

When the user wants to create or optimize popups, modals, overlays, slide-ins, or banners for conversion purposes. Also use when the user mentions "exit intent," "popup conversions," "modal optimization," "lead capture popup," "email popup," "announcement banner," or "overlay." For forms outside of popups, see form-cro. For general page conversion optimization, see page-cro.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

When the user wants to create or optimize in-app paywalls, upgrade screens, upsell modals, or feature gates. Also use when the user mentions "paywall," "upgrade screen," "upgrade modal," "upsell," "feature gate," "convert free to paid," "freemium conversion," "trial expiration screen," "limit reached screen," "plan upgrade prompt," or "in-app pricing." Distinct from public pricing pages (see page-cro) — this skill focuses on in-product upgrade moments where the user has already experienced value.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Execute model inference on GPU cloud providers. Handles code generation, deployment, execution, and result collection across HF Inference API/Endpoints, Colab, Modal, beam.cloud, Vast.ai, and RunPod. Use when running models on GPU, deploying to cloud, executing notebooks, or troubleshooting GPU execution failures. Triggers on "run on GPU", "execute model", "deploy to modal", "colab notebook", "beam deploy", "HF inference", "HF endpoints", "vast", "runpod".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert guidance for fine-tuning LLMs with LLaMA-Factory - WebUI no-code, 100+ models, 2/3/4/5/6/8-bit QLoRA, multimodal support

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.