「audio」の検索結果

Discover and reuse owner resource structure, then validate paths, RSI, audio, maps, attribution, and localization companions.

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 common EntitySystem helpers, prototype APIs, spawning, deletion, audio, popup, and random patterns.

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。

Download YouTube videos with customizable quality and format options. Use this skill when the user asks to download, save, or grab YouTube videos. Supports various quality settings (best, 1080p, 720p, 480p, 360p), multiple formats (mp4, webm, mkv), and audio-only downloads as MP3.

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 this skill when the user requests to generate, create, or produce podcasts from text content. Converts written content into a two-host conversational podcast audio format with natural dialogue.

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。

Azure OpenAI SDK for .NET. Client library for Azure OpenAI and OpenAI services. Use for chat completions, embeddings, image generation, audio transcription, and assistants.

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 this skill to understand FMCSA hearing standards for CMV drivers. Covers the forced whisper test, audiometry alternative, and certification for hearing-impaired drivers.

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。

Discover failure modes in Coval runs, uploaded conversations or local conversation exports using scoped sampling and transcript, audio and trace evidence. Prepare human review without inventing ground truth or launching new runs.

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。

Select and configure a Coval metric for a concrete product criterion, choosing transcript, audio, trace or deterministic evidence and testing the result. Use for metric creation or scoring design, not for final human calibration.

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。

Summarize URLs or files with the summarize CLI (web, PDFs, images, audio, YouTube). Use when you need fast CLI-based summaries from local or remote sources.

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。

Troubleshoot and debug LiveAvatar integration issues. Use when the user's LiveAvatar integration isn't working, the avatar is silent, audio is garbled, sessions fail to start, events aren't received, or they're getting API errors.

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。

Expert in statistical analysis for blind A/B and ABX audio testing. Validates randomization, calculates statistical significance, and ensures proper experimental design. Use when implementing A/B test features or analyzing test results.

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。

Comprehensive WCAG accessibility auditing with multi-tool testing (axe-core + pa11y + Lighthouse), TRUE PARALLEL execution with Promise.allSettled, graceful degradation, retry with backoff, context-aware remediation, learning integration, and video accessibility. Uses 3-tier browser cascade: Vibium → agent-browser → Playwright+Stealth.

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。

ALWAYS use this skill when user needs ANY API functionality (AI models, image generation, video, audio, text processing, etc.). Automatically search 302.AI's 1400+ APIs and generate integration code. Use proactively whenever APIs or AI capabilities are mentioned.

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。

Azure OpenAI SDK for .NET. Client library for Azure OpenAI and OpenAI services. Use for chat completions, embeddings, image generation, audio transcription, and assistants.

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 this skill when previewing files with nuxt-ui-tools as a package consumer. Covers mounting UiFilePreviewProvider, opening one file or a gallery with useFilePreview() or useNuxtApp().$filePreview, file descriptors (URL, File/Blob, or a function that returns a signed URL), options (index, mode, gallery, loop, pdf page, callbacks), the returned handle, the built-in renderers (image, video, audio, PDF, text/JSON, CSV, Markdown, Office and archive fallbacks), renditions, download and custom header actions, the markdown render hook, custom renderers with defineFilePreviewRenderer and UiFilePreviewTools, theming tokens, and how the upload field uses the preview.

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。

OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.

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 this skill when the user requests to generate, create, or produce podcasts from text content. Converts written content into a two-host conversational podcast audio format with natural dialogue.

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。

GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.

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。

Download YouTube videos with customizable quality and format options. Use this skill when the user asks to download, save, or grab YouTube videos. Supports various quality settings (best, 1080p, 720p, 480p, 360p), multiple formats (mp4, webm, mkv), and audio-only downloads as MP3.

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。

Convert files and office documents to Markdown. Supports PDF, DOCX, PPTX, XLSX, images (with OCR), audio (with transcription), HTML, CSV, JSON, XML, ZIP, YouTube URLs, EPubs and more.

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。

Work with state-of-the-art machine learning models for NLP, computer vision, audio, and multimodal tasks using HuggingFace Transformers. This skill should be used when fine-tuning pre-trained models, performing inference with pipelines, generating text, training sequence models, or working with BERT, GPT, T5, ViT, and other transformer architectures. Covers model loading, tokenization, training with Trainer API, text generation strategies, and task-specific patterns for classification, NER, QA, summarization, translation, and image tasks. (plugin:scientific-packages@claude-scientific-skills)

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。

Expert skill for building web-based game engines and games using HTML5, Canvas, WebGL, and JavaScript. Use when asked to create games, build game engines, implement game physics, handle collision detection, set up game loops, manage sprites, add game controls, or work with 2D/3D rendering. Covers techniques for platformers, breakout-style games, maze games, tilemaps, audio, multiplayer via WebRTC, and publishing games.

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。