「modal」の検索結果

Render an HTML/CSS/JS template to local MP4 via headless Chromium (Playwright) + ffmpeg. NOT live browser recording, screen recording, or AI video gen (mk:multimodal).

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 a plan directory (plan.md + phase-*.md) needs a visual review. By DEFAULT this drives the STRUCTURED visual-plan artifact (visual-plan/plan.json) via the mewkit visual-plan CLI — generate if absent, validate, open the local studio (view/edit), and export plan.html from the approved artifact. Pass --static for the legacy prompt-only single-file plan.html template render. Triggers on "render this plan", "visualize the plan", "open the plan studio", "make a shareable plan page", "plan.html". This is the canonical owner of plan-as-visual-review. NOT for plan critique or scope review (see mk:plan-ceo-review / mk:validate-plan), NOT for generic code/diagram/diff visuals (see mk:preview), NOT for live media generation (see mk:multimodal).

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 generating visual artifacts — explanations, diagrams, slides, or diff visualizations. Triggers on "explain X visually", "diagram this", "show as slides", "diff against main". NOT for rendering a plan as HTML (see mk:visual-plan), live media generation (see mk:multimodal), browser QA (see mk:qa), or plan critique (see mk:plan-ceo-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。

Render an HTML/CSS/JS template to a local MP4 video by capturing frames with headless Chromium (via Playwright) and encoding them with ffmpeg. Use when "render my animation as a video", "export HTML to MP4", "make a video from my web template", or "capture CSS animation as MP4". NOT for recording live browser interactions (use `agent-browser record start`); NOT for screen recording; NOT for AI video generation (use mk:multimodal).

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。

Rembric dashboard UI work (dark monochrome zinc surfaces, lime accent, Geist type, Spectrum data-tables) — building / editing / extending pages, styles, components, modals, tables, filters, pagination, mobile responsive, destructive confirmations. Use when the user asks for changes under `apps/web/src/app/dashboard/` or `apps/web/src/components/dashboard/`, mentions Rembric design tokens (lime, zinc, command bar, StatusPill, hl-lime, data-table), or requests a new dashboard page / form / table.

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。

Advanced CV for infrastructure inspection including forest fire detection, wildfire precondition assessment, roof inspection, hail damage analysis, thermal imaging, and 3D Gaussian Splatting reconstruction. Expert in multi-modal detection, insurance risk modeling, and reinsurance data pipelines. Activate on "fire detection", "wildfire risk", "roof inspection", "hail damage", "thermal analysis", "Gaussian Splatting", "3DGS", "insurance inspection", "defensible space", "property assessment", "catastrophe modeling", "NDVI", "fuel load". NOT for general drone flight control, SLAM, path planning, or sensor fusion (use drone-cv-expert), GPU shader development (use metal-shader-expert), or generic object detection without inspection context (use clip-aware-embeddings).

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。

Implement executable BDI reasoning with explicit negation, paraconsistent revision, trigger-based commitment updates, and abduction. Use for runtime agent semantics and conflicting desires. NOT for purely axiomatic modal logic, black-box planners, or classical logic without operational semantics.

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。

Semantic image-text matching with CLIP and alternatives. Use for image search, zero-shot classification, similarity matching. NOT for counting objects, fine-grained classification (celebrities, car models), spatial reasoning, or compositional queries. Activate on "CLIP", "embeddings", "image similarity", "semantic search", "zero-shot classification", "image-text matching".

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。

Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.

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。

Designs and analyzes pooled prime-editor (PE) screens for installing precise genetic variants without bystander confounding. Covers pegRNA design with PRIDICT and PRIDICT2 for predicting per-pegRNA editing efficiency, pegRNA architecture (spacer + scaffold + PBS + RTT), PE2/PE3/PE3b/PEmax variants, MOSAIC in situ saturation mutagenesis, the PRIME pooled-screen methodology (Ren 2023; ~3,699 ClinVar variant screens), chromatin context as a major locus-level determinant of PE efficiency, scaffold-incorporation and indel byproduct quantification with CRISPResso2, and the cross-modal validation strategy of PE + base-editor screens for variant function. Use when designing a pegRNA library for variant installation, choosing between BE and PE for a specific edit, predicting pegRNA efficiency before library synthesis, analyzing PE screen output, distinguishing intended-edit from scaffold-incorporation, or scaling PE screens to thousands of variants.

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。

Predict RBP binding from RNA sequence using deep learning models (RBPNet sequence-to-signal, RNAProt RNN, GraphProt2 GCN with structure, DeepCLIP, DeepRiPe multi-modal CNN) for variant-effect prediction, in silico binding-site discovery, model interpretation, and transfer learning from CLIP and RBNS datasets. Use when computational prediction of RBP binding from sequence is needed, evaluating variant effects on binding without further wet-lab experiments, comparing model performance, or training a custom model on ENCODE eCLIP data.

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。

This skill recommends computed tomography (CT) as the initial imaging modality for primary aldosteronism (PA) evaluation in Japan, citing its accessibility and comparable performance to MRI. It is triggered when a clinician orders imaging for suspected PA and asks 'What imaging should I start with?' or seeks a cost-effective initial evaluation.

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。

Azure AI Content Understanding SDK for Python. Use for multimodal content extraction from documents, images, audio, and video.

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。

Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.

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。

Build React animations with Motion (Framer Motion) - gestures (drag, hover, tap), scroll effects, spring physics, layout animations, SVG. Bundle: 2.3 KB (mini) to 34 KB (full). Use when: drag-and-drop, scroll animations, modals, carousels, parallax. Troubleshoot: AnimatePresence exit, list performance, Tailwind conflicts, Next.js "use 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。

This skill should be used when designing terminal user interfaces, creating TUI layouts, choosing TUI color schemes, implementing keyboard navigation, building terminal dashboards, or working with any TUI framework. Activates on mentions of TUI design, terminal UI, Ratatui layout, Ink components, Textual widgets, Bubbletea views, terminal color palette, keybinding design, panel layout, split panes, terminal dashboard, box-drawing characters, sparklines, progress bars, modal dialogs, focus management, or terminal accessibility.

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。

Inteligência de design UI/UX. 50 estilos, 21 paletas, 50 combinações de fontes, 20 gráficos, 9 stacks (React, Next.js, Vue, Svelte, SwiftUI, React Native, Flutter, Tailwind, shadcn/ui). Ações: plan, build, create, design, implement, review, fix, improve, optimize, enhance, refactor, check UI/UX code. Projetos: website, landing page, dashboard, admin panel, e-commerce, SaaS, portfolio, blog, mobile app, .html, .tsx, .vue, .svelte. Elementos: button, modal, navbar, sidebar, card, table, form, chart. Estilos: glassmorphism, claymorphism, minimalism, brutalism, neumorphism, bento grid, dark mode, responsive, skeuomorphism, flat design. Tópicos: color palette, accessibility, animation, layout, typography, font pairing, spacing, hover, shadow, gradient. Integrações: shadcn/ui MCP para busca de componentes e exemplos.

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。

Quando o usuário quer criar ou otimizar paywalls in-app, telas de upgrade, modais de upsell ou feature gates. Use também quando o usuário mencionar "paywall", "tela de upgrade", "modal de upgrade", "upsell", "feature gate", "converter free para paid", "conversão freemium", "tela de expiração de trial", "tela de limite atingido", "prompt de upgrade de plano" ou "preço in-app". Distinto de páginas de preço públicas (veja page-cro) — esta skill foca em momentos de upgrade in-product onde o usuário já experimentou valor.

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。

Quando o usuário deseja criar ou otimizar popups, modais, overlays, slide-ins ou banners para fins de conversão. Use também quando o usuário menciona "exit intent," "conversões de popup," "otimização de modal," "popup de captura de leads," "popup de email," "banner de anúncio" ou "overlay." Para formulários fora de popups, consulte form-cro. Para otimização geral de conversão de página, consulte page-cro.

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。

Quando o usuário quer otimizar, melhorar ou aumentar conversões em qualquer página de marketing — incluindo homepage, landing pages, páginas de preços, páginas de features ou posts de blog. Também use quando o usuário menciona "CRO," "otimização de taxa de conversão," "essa página não está convertendo," "melhorar conversões," ou "por que essa página não funciona." Para fluxos de signup/registro, veja signup-flow-cro. Para ativação pós-signup, veja onboarding-cro. Para formulários fora de signup, veja form-cro. Para popups/modals, veja popup-cro.

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。

Framework para embeddings de última geração em sentenças, textos e imagens. Oferece 5000+ modelos pré-treinados para similaridade semântica, clustering e retrieval. Suporta modelos multilíngues, específicos de domínio e multimodais. Use para gerar embeddings para RAG, busca semântica ou tarefas de similaridade. Melhor para geração de embeddings em produção.

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。

Modelo de fundação para segmentação de imagens com transferência zero-shot. Use quando precisar segmentar qualquer objeto em imagens usando pontos, caixas ou máscaras como prompts, ou gerar automaticamente todas as máscaras de objetos em uma imagem.

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。

Biblioteca PyTorch para geração de áudio incluindo text-to-music (MusicGen) e text-to-sound (AudioGen). Use quando precisar gerar música a partir de descrições de texto, criar efeitos sonoros ou realizar geração de música condicionada por melodia.

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。

Assistente de Linguagem Grande e Visão. Habilita ajuste de instrução visual e conversas baseadas em imagem. Combina encoder de visão CLIP com modelos de linguagem Vicuna/LLaMA. Suporta chat multi-turno com imagem, resposta a perguntas visuais e seguimento de instruções. Use para chatbots visão-linguagem ou tarefas de compreensão de imagem. Melhor para análise conversacional de imagem.

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。