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Found 30 Skills
Asset preprocessing for HyperFrames compositions — text-to-speech narration (Kokoro), audio/video transcription (Whisper), and background removal for transparent overlays (u2net). Use when generating voiceover from text, transcribing speech for captions, removing the background from a video or image to use as a transparent overlay, choosing a TTS voice or whisper model, or chaining these (TTS → transcribe → captions). Each command downloads its own model on first run.
Transcribe audio to text with Whisper models via inference.sh CLI. Models: Fast Whisper Large V3, Whisper V3 Large. Capabilities: transcription, translation, multi-language, timestamps. Use for: meeting transcription, subtitles, podcast transcripts, voice notes. Triggers: speech to text, transcription, whisper, audio to text, transcribe audio, voice to text, stt, automatic transcription, subtitles generation, transcribe meeting, audio transcription, whisper ai
Transcribe audio to text using ElevenLabs Scribe v2. Use when converting audio/video to text, generating subtitles, transcribing meetings, or processing spoken content.
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.
Podcast content editing. Generate verbatim transcripts with speakers and timestamps, and AI marks content suggested for deletion (small talk, off-topic ramblings, redundant content, privacy-related information). Trigger words: content edit, cut content, content edit
Read, watch, and listen to video/audio files. Extract key frames to "see" videos, extract audio to "hear" them via Whisper transcription. Use when a user sends a video/audio and asks about its content, what's in it, what someone said, etc.
Video understanding for any model — native passthrough for small files, frame extraction + audio transcription fallback for large files. Use when the user asks to analyze, describe, or understand a video file (e.g. "what's in this video", "summarize this clip", "transcribe this recording").
Use local FunASR service to transcribe audio or video files into timestamped Markdown files, supporting common formats such as mp4, mov, mp3, wav, m4a, etc. This skill should be used when users need speech-to-text conversion, meeting minutes, video subtitles, or podcast transcription.
Transcribe audio and video files using the Deepgram API. This skill should be used when the user requests transcription of audio files (mp3, wav, m4a, aac) or video files (mp4, mov, avi, etc.). Handles large video files by extracting audio first to reduce upload size and processing time.
Convert local documents to Markdown using Microsoft's markitdown CLI. Best for: PDF, Word, Excel, PowerPoint, images (OCR), audio. Can fetch URLs but Jina is faster for web. Triggers on: convert to markdown, read PDF, parse document, extract text from, docx, xlsx, pptx, OCR image, local file.
Multimodal AI processing via Google Gemini API (2M tokens context). Capabilities: audio (transcription, 9.5hr max, summarization, music analysis), images (captioning, OCR, object detection, segmentation, visual Q&A), video (scene detection, 6hr max, YouTube URLs, temporal analysis), documents (PDF extraction, tables, forms, charts), image generation (text-to-image, editing). Actions: transcribe, analyze, extract, caption, detect, segment, generate from media. Keywords: Gemini API, audio transcription, image captioning, OCR, object detection, video analysis, PDF extraction, text-to-image, multimodal, speech recognition, visual Q&A, scene detection, YouTube transcription, table extraction, form processing, image generation, Imagen. Use when: transcribing audio/video, analyzing images/screenshots, extracting data from PDFs, processing YouTube videos, generating images from text, implementing multimodal AI features.
Use when implementing speech-to-text, audio transcription, real-time streaming STT, audio intelligence features, or voice AI using AssemblyAI APIs or SDKs. Use when user mentions AssemblyAI, voice agents, transcription, speaker diarization, PII redaction of audio, LLM Gateway for audio understanding, or applying LLMs to transcripts. Also use when building voice agents with LiveKit or Pipecat that need speech-to-text, or when the user is working with any audio/video processing pipeline that could benefit from transcription, even if they don't mention AssemblyAI by name.