「modal」の検索結果

Geração de imagens com tecnologia de ponta usando Stable Diffusion via HuggingFace Diffusers. Use quando gerar imagens a partir de prompts em texto, realizar tradução de imagem para imagem, inpainting ou construir pipelines de difusão customizados.

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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.

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Processamento escalável de dados para workloads de ML. Execução em streaming em CPU/GPU, suporta Parquet/CSV/JSON/imagens. Integra-se com Ray Train, PyTorch, TensorFlow. Escala de uma única máquina para centenas de nós. Use para inferência em lote, pré-processamento de dados, carregamento de dados multi-modal ou pipelines distribuídos de ETL.

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Curação de dados acelerada por GPU para treinamento de LLM. Suporta texto/imagem/vídeo/áudio. Recursos incluem deduplicação fuzzy (16× mais rápida), filtragem de qualidade (30+ heurísticas), deduplicação semântica, redação de PII, detecção NSFW. Escala em GPUs com RAPIDS. Use para preparar datasets de treinamento de alta qualidade, limpar dados web ou deduplicar grandes corpora.

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Orientação especializada para fine-tuning de LLMs com Axolotl - configs YAML, 100+ modelos, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, suporte multimodal

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Load this skill for every project containing interactive UI elements — buttons, links, modals, dropdowns, sliders, tabs, carousels, or any custom widget. Under no circumstances create an interactive component that cannot be fully operated by keyboard alone. Absolutely always ensure visible focus indicators, logical tab order, and no keyboard traps. Apply these rules to every interactive element without exception.

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UI/UX design intelligence. 67 styles, 96 palettes, 57 font pairings, 25 charts, 13 stacks (React, Next.js, Vue, Svelte, SwiftUI, React Native, Flutter, Tailwind, shadcn/ui). Actions: plan, build, create, design, implement, review, fix, improve, optimize, enhance, refactor, check UI/UX code. Projects: website, landing page, dashboard, admin panel, e-commerce, SaaS, portfolio, blog, mobile app, .html, .tsx, .vue, .svelte. Elements: button, modal, navbar, sidebar, card, table, form, chart. Styles: glassmorphism, claymorphism, minimalism, brutalism, neumorphism, bento grid, dark mode, responsive, skeuomorphism, flat design. Topics: color palette, accessibility, animation, layout, typography, font pairing, spacing, hover, shadow, gradient. Integrations: shadcn/ui MCP for component search and examples.

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Add image vision to NanoClaw agents. Resizes and processes WhatsApp image attachments, then sends them to Claude as multimodal content blocks.

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。

Add image vision to NanoClaw agents. Resizes and processes WhatsApp image attachments, then sends them to Claude as multimodal content blocks.

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。

Convex UI component patterns from the live Storybook preview. Use when building React components, forms, modals, navigation, feedback states, or app layouts that should match the current Convex design system. Applies to both shared primitives and dashboard style product UI.

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。

Convex UI component patterns from the live Storybook preview. Use when building React components, forms, modals, navigation, feedback states, or app layouts that should match the current Convex design system. Applies to both shared primitives and dashboard style product UI.

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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), pr...

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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.

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Create aesthetically beautiful interfaces following proven design principles. Use when building UI/UX, analyzing designs from inspiration sites, generating design images with ai-multimodal, implementing visual hierarchy and color theory, adding micro-interactions, or creating design documentation...

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Detect and defend against prompt injection hidden in documents, web pages, and images consumed by an agent.

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Use when building styled UI with @nuxt/ui v4 components (Button, Modal, Form, Table, etc.) - provides ready-to-use components with Tailwind Variants theming. Use vue skill for raw component patterns, reka-ui for headless primitives.

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Remote-sensing literature search, citation verification, sensor/platform/dataset query strategy, citation file management (.ris/.bib/.enw conversion), and reference management (BibTeX, related articles, DOI/arXiv lookup) across CrossRef, arXiv, Scopus, ScienceDirect, Semantic Scholar, and domain web sources. Use when the user needs coordinated multi-step literature workflows for remote sensing, Earth observation, geospatial AI, GIScience, photogrammetry, SAR, hyperspectral imaging, LiDAR, satellite missions, benchmark datasets, land-cover mapping, change detection, object detection, segmentation, retrieval, or multimodal remote-sensing foundation models. Also trigger on general literature-search needs during academic writing even without the word "Nature", such as searching for papers/literature, doing a literature review, verifying a citation, converting citation files, and Chinese phrasings like 文献检索、查文献、找文献、 文献综述检索、查论文、引文核对、参考文献管理、文献去重.

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Operational guide for integrating the NV oOS Complete purchase flow (vendor checkout API client) into standalone WordPress plugins — docs-hub 0.5.3 (stack added in PR #6963, shipped 0.5.2 → 0.5.3) and nvoos-content-graph 1.0.10 (live). Covers the vendor API contract (session/verify/health, nvoos-oos-complete product, 424/502 error passthrough), the five-class client stack (config, vendor client, license store, installer, REST controller), the purchase-modal JS/CSS port recipe (class-prefix rename, keep-the-JS-identical trick), wp.org commerce compliance (External Services disclosure, WPORG-REVIEW-COMMERCE-NOTES.md, js.stripe.com service exception), and the checkout test conventions (direct controller calls, pi_ fixture rules, zero-HTTP assertions). Use when adding the Get NV oOS Complete upsell/checkout to a plugin, porting the commerce stack to a new standalone plugin, extending the purchase flow, or debugging a checkout integration.

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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。

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".

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Hierarchical, Hybrid, and Omnimodal Models Jun 30 58 m Jun 30, 2026 58 m read Show 10 25 50 100 pe GAP: no mappable capability in latest signal (our t

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Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder, dense or static embedding model for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker, pair scoring for two-stage retrieval / pair classification), `SparseEncoder` (SPLADE, sparse embedding model for learned-sparse retrieval), and `MultiVectorEncoder` (ColBERT / late-interaction, per-token embeddings scored with MaxSim). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.

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。