Results for “multimodal”

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.

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Expert guide for multimodal vision UI auditing (Gemini 4 Pro / Flash Vision, Claude 3.7 / 5.1 Sonnet, GPT-5 Vision) against Anti-Slop Pillar 7, 1px crisp borders, 3-tier radius scales, tabular numerals, and WCAG 2.2 AA / Panduan ahli audit visual UI berbasis vision multimodal.

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Master optimization protocol for Gemini Agent (Antigravity) to unlock native 2M+ to 10M+ long-context reasoning, Gemini 4 Pro & Gemini 4 Flash dynamic thinking budget control, native context caching, Multimodal Live API, and high-speed problem solving / Protokol optimasi utama untuk Gemini Agent (Antigravity) untuk mengaktifkan pemikiran long-context 2M+ hingga 10M+, kontrol thinking budget dinamis Gemini 4 Pro & Gemini 4 Flash, context caching native, Multimodal Live API, dan pemecahan masalah kecepatan tinggi.

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Expert guide for Affective Computing, emotional AI, and real-time sentiment analysis through native multimodal tokens (voice intonation and facial micro-expressions) / Panduan ahli komputasi afektif, AI emosional, dan analisis sentimen real-time melalui token multimodal native.

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Analyze and extract information from images using multimodal AI recognition. Triggered when users want to analyze, describe, or extract information from an image URL — image recognition, image analysis, image description, visual content understanding, OCR text recognition, or visual Q&A. When a user provides an image URL and asks questions about its visual content, this skill should be triggered even if they do not explicitly say "image recognition."

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Analyze images/audio/video with Gemini API (better vision than Codex). Generate images (Imagen 4), videos (Veo 3). Use for vision analysis, transcription, OCR, design extraction, multimodal AI.

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Use SimpleMem to store, compress, index, and retrieve text or multimodal memories for agents through MCP or Python integrations.

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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), 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.

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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.

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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.

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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), 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.

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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.

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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

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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.

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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.

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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".

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

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Turn visual preferences and local references into reusable style packs, validated settings for look and rhythm. Plan cuts for local footage, export editable timelines, and prepare image, video, or 3D asset plans without calling generation providers.

• Interview for visual direction and create a reusable style pack
• Validate style packs and audit their sources
• Plan local footage cuts using measured reference rhythm

Extract and verify text, numbers, objects, and sequences from images, documents, and video. Record where each finding appears, check exact labels and values, and build answers whose supporting evidence can be traced back to the input.

• Extracting and checking numbers in documents
• Tracing video events to timestamps
• Reading and verifying labels in images

Generate 4–30 second videos with synchronized audio using ByteDance Seedance 2.5. Organize image, video, and audio references, write shot-by-shot prompts, and define continuity rules to keep characters and locations consistent across cuts.

• Generating short videos with synchronized audio
• Planning multi-shot scenes with consistent characters and locations
• Building video prompts from image, video, and audio references

Use Hugging Face Transformers to load pretrained models, generate text, and classify text, images, or audio. Prepare model inputs, control generation settings, and fine-tune models on custom datasets with the Trainer API.

• Prototype text, image, or audio classification
• Configure text generation and conversational responses
• Fine-tune a model on a custom dataset

Fit probabilistic models to single-cell measurements for integration across experiments, cell annotation, combined RNA and protein or ATAC analysis, and gene expression comparisons. The skill covers input requirements, training checks, reference mapping, and interpretation of model uncertainty.

• Integrating single-cell RNA data across experimental batches
• Assigning cell labels using partial annotations or a reference
• Analyzing RNA with protein or ATAC measurements

Helps build and review NeuroKit2 research workflows for physiological recordings such as ECG, respiration, and skin conductance. Covers preprocessing, time alignment, event analysis, heart rate variability, and complexity measures, while recording methods and checking data quality.

• Inspect physiological recordings before preprocessing
• Analyze ECG and heart rate variability for research
• Plan event windows and baseline correction

Extract text and its page coordinates locally from PDFs, Office documents, and images. Use OCR to read scans, export structured JSON or Markdown, process document folders, and render pages as PNG images for agents that analyze visual content.

• Extracting text from scanned PDFs and images
• Preparing document text and coordinates for retrieval-based AI answers
• Batch-processing folders of research papers