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概要と使いどころ
Support automatic audio switching for paired third-party Bluetooth headphones or earbuds with AudioAccessoryKit. Use when a companion app registers an audio accessory, an app extension reports worn/removed placement or connected source-device changes, or AccessoryControlDevice capabilities and errors need handling. Do not use for general AVAudioSession routing, Bluetooth transport, or initial accessory pairing.
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 implementing audio — audio buses, AudioStreamPlayer, spatial audio, music management, SFX pooling, and dynamic mixing
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。
Volcengine VOD audio and video processing tools skill. Use when users need VOD-based media processing or editing: upload local/URL media, stitch videos, trim clips, flip frames, change playback speed, create image-to-video, compose audio/video, extract audio, mix audio, separate vocals/accompaniment, denoise audio, enhance quality, AI super-resolution, frame interpolation, ASR speech-to-text, OCR text extraction, subtitle removal, subtitle embedding, scene slicing, portrait/green-screen matting, highlight extraction, comic style transfer, video translation, drama recap narration, drama script restoration, media info lookup, or playback URL retrieval. The skill submits async VOD jobs, polls task status, and returns generated output links. Not for pure text generation, real-time streaming, or source-free generative video creation.
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 documents and text to audio using Google Cloud Text-to-Speech. Use this skill when the user wants to: narrate a document, read aloud text, generate audio from a file, convert text to speech, create a recording of documentation or analysis, create a podcast from a document, or use Google TTS/text-to-speech. Trigger phrases: "read this aloud", "narrate this", "create a recording", "text to speech", "TTS", "convert to audio", "audio from document", "listen to this", "generate audio", "google tts", "create a podcast".
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。
Video/audio/image processing with FFmpeg and ImageMagick. Tools: FFmpeg (video/audio), ImageMagick (images). Capabilities: format conversion, encoding (H.264/H.265/VP9/AV1), streaming (HLS/DASH), filters, effects, thumbnails, watermarks, batch processing, hardware acceleration (NVENC/QSV). Actions: convert, encode, resize, crop, compress, extract, merge, stream, transcode media. Keywords: FFmpeg, ImageMagick, video encoding, audio extraction, image resize, thumbnail, watermark, HLS, DASH, H.264, H.265, VP9, AV1, codec, bitrate, framerate, resolution, aspect ratio, filter, overlay, concat, trim, fade, batch processing. Use when: converting video/audio formats, encoding with specific codecs, generating thumbnails, creating streaming manifests, extracting audio from video, batch processing images, adding watermarks, optimizing file sizes.
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 patterns for AI-powered audio generation including text-to-music, voice synthesis, text-to-speech, sound effects, and audio manipulation using MusicGen, Bark, ElevenLabs, and more. Use when "music generation, text to music, AI music, voice cloning, text to speech, TTS API, ElevenLabs, MusicGen, Bark, audio synthesis, sound effects generation, voice synthesis, AudioCraft, " 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。
Check for Faster-Whisper (preferred - 4-5x faster) workflow skill. Use this skill when the user needs Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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 videos, audio files, and podcasts into key takeaways. Give it a YouTube/podcast URL or a local audio/video file — it transcribes locally (cached; repeat requests reuse the transcript) and distills the transcript into key points, summaries, or translations. Use when the user asks to summarize, get key points from, translate, or TL;DR any audio or video content.
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。
Step-by-step guide for building a YUP audio plugin from scratch. Use when the user asks "how do I create a YUP plugin", "set up a YUP project", "add parameters to my YUP plugin", "package my CLAP or VST3", or "how do I wire up AudioParameterHandle". Trigger on phrases like "create a YUP plugin", "YUP CMake setup", "yup_audio_plugin", "AudioParameterBuilder", "AudioParameterHandle", or "createPluginProcessor".
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。
Reviews AudioUnit v2/v3 plugin implementations for spec compliance, thread safety, and correctness. Use when the user asks to review an AudioUnit plugin, check an AUv3 app extension, audit property or parameter handling, or asks "is my AUComponent thread-safe?" or "why does my AU crash in Logic?". Trigger on phrases like "review my AudioUnit", "check my AUv3", "is my render block safe?", or when you see AURenderCallback, internalRenderBlock, kAudioUnitProperty_*, or AUAudioUnit in the code.
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。
Reviews JUCE audio plugin code for JUCE-specific correctness issues: thread safety, APVTS parameter patterns, MessageManager usage, ValueTree, and MIDI handling. Use when the user asks to review a JUCE plugin, check a processBlock, audit parameter handling, or asks "is this JUCE code safe?". Trigger on phrases like "review my JUCE plugin", "check my AudioProcessor", "is this APVTS usage correct?", or when you see AudioProcessor, AudioProcessorEditor, or AudioProcessorValueTreeState in the code.
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。
Annotate speech/TTS datasets with per-clip quality metrics (MOS, SNR, STOI, pitch, bandwidth, speaking rate, WER/CER, gender/emotion), multi-ASR consensus transcription and speaker labeling using the audiogear pipeline; then filter clips by quality and build listenable HTML QA reports. Use when asked to compute audio dataset metrics, build extended_metadata.csv, transcribe unlabeled audio, select clean TTS training data, or QA a speech dataset.
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 audio transcription pipelines with Whisper, Deepgram, and AssemblyAI including speaker diarization and real-time streaming. Activate on: transcription, speech-to-text, diarization, audio processing, meeting transcripts. NOT for: text-to-speech synthesis (voice-audio-engineer), music generation (ai-engineer).
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 2000s-era music visualization (Milkdrop, AVS, Geiss) and modern WebGL implementations. Specializes in Butterchurn integration, Web Audio API AnalyserNode FFT data, GLSL shaders for audio-reactive visuals, and psychedelic generative art. Activate on "Milkdrop", "music visualization", "WebGL visualizer", "Butterchurn", "audio reactive", "FFT visualization", "spectrum analyzer". NOT for simple bar charts/waveforms (use basic canvas), video editing, or non-audio visuals.
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。
Load this skill whenever the project contains audio or video content, media players, podcasts, video embeds, or any <audio>/<video> elements. Under no circumstances publish audio or video without captions, transcripts, and audio descriptions where required. Absolutely always apply WCAG 1.2 criteria for time-based media.
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。
Detects AI-generated deepfake audio used in voice phishing (vishing) attacks by extracting spectral features (MFCC, spectral centroid, spectral contrast, zero-crossing rate) and classifying samples with machine learning models. Supports batch analysis of audio files, generates confidence scores, and produces forensic reports. Activates for requests involving deepfake voice detection, vishing investigation, AI-generated speech analysis, voice cloning detection, or audio authenticity verification.
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 en AudioCraft avancé (MusicGen text-to-music, AudioGen text-to-sound, fine-tuning)
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。
Detects AI-generated deepfake audio used in voice phishing (vishing) attacks by extracting spectral features (MFCC, spectral centroid, spectral contrast, zero-crossing rate) and classifying samples with machine learning models. Supports batch analysis of audio files, generates confidence scores, and produces forensic reports. Activates for requests involving deepfake voice detection, vishing investigation, AI-generated speech analysis, voice cloning detection, or audio authenticity verification.
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。
PyTorch library for audio generation including text-to-music (MusicGen) and text-to-sound (AudioGen). Use when you need to generate music from text descriptions, create sound effects, or perform melody-conditioned music generation.
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。
End-to-end Coval audio-quality testing workflow. Launches one run per audio-robustness scenario against the same voice agent + test set + metrics, polls for completion, and produces the multi-run report URL grouped by Persona. Use when a user wants to follow the Testing Across Audio Qualities cookbook (https://docs.coval.dev/guides/testing-across-audio-qualities) without doing each step by hand.
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。
The TTS/STT/audio-playback system behind the one `speak()` entry point. Use when working on read-aloud, the Listen panel, voice/speed settings, transcription, mic capture, SpeakerButton, iOS playing silence, audio API routes, or files in features/audio/, features/tts/, hooks/tts/, app/api/audio/.
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 2000s-era music visualization (Milkdrop, AVS, Geiss) and modern WebGL implementations. Specializes in Butterchurn integration, Web Audio API AnalyserNode FFT data, GLSL shaders for audio-reactive visuals, and psychedelic generative art. Activate on "Milkdrop", "music visualization", "WebGL visualizer", "Butterchurn", "audio reactive", "FFT visualization", "spectrum analyzer". NOT for simple bar charts/waveforms (use basic canvas), video editing, or non-audio visuals.
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。
Detects AI-generated deepfake audio used in voice phishing (vishing) attacks by extracting spectral features (MFCC, spectral centroid, spectral contrast, zero-crossing rate) and classifying samples with machine learning models. Supports batch analysis of audio files, generates confidence scores, and produces forensic reports. Activates for requests involving deepfake voice detection, vishing investigation, AI-generated speech analysis, voice cloning detection, or audio authenticity verification.
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。