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Find YouTube videos, retrieve caption transcripts, browse channels, and list playlist contents through TranscriptAPI. Use the results to summarize videos, extract quotes, check new uploads, or build a searchable collection of transcript documents.
Analyze, summarize, and extract insights from DeLive transcription sessions. Use when: user mentions DeLive, transcription, meeting transcripts, live captions, audio transcription, AI correction, corrected transcript, or transcript analysis; user wants to search, retrieve, summarize, correct, or process recorded transcripts; user asks about meeting notes, action items, discussion summaries, or transcript quality from DeLive. Requires DeLive app running locally with its MCP server or REST API.
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Use Azure's Python transcription client to turn audio files into text and review timestamped results. Covers authentication, installed SDK options, and error handling, while clarifying when streaming or asynchronous batch work needs a different Speech API.
Build transcription workflows for uploaded or stored audio and video using Zoom AI Services Scribe. Consult guidance on authentication, single-file requests, batch jobs, completion notifications, and saving transcript results for further processing.
YouTube transcript extraction and content reformatting: given a YouTube video URL, opens the video's transcript panel, extracts all timestamped segments, and transforms the raw transcript into summaries, chapter outlines, Twitter/X threads, blog posts, or notable quotes. Use when the user shares a YouTube URL or video link, asks to summarize a video, get a transcript, extract content from a YouTube video, get YouTube captions, extract YouTube captions, download YouTube captions, transcribe YouTube video, YouTube video to text, make a thread from YouTube, YouTube to blog post, YouTube to article, pull transcript from YouTube, YouTube content extraction, convert YouTube to text, video to transcript. Also applies when user wants to reformat any YouTube video content into structured output (chapters, threads, blog articles, key quotes).
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This skill helps users automatically extract YouTube video transcripts and metadata in batch via the BrowserAct API. The Agent should proactively apply this skill when users express needs like batch extract full transcripts from YouTube videos for specific keywords, scrape YouTube subtitles for a list of videos, get batch video metadata and likes counts for analysis, automate YouTube search and subtitle extraction, collect multiple video transcripts published this week, download bulk YouTube video subtitles without writing crawler scripts, build a dataset of transcripts from top YouTube videos, extract YouTube video URLs and publisher info in batch, gather full video content for AI summarization pipelines, monitor recent YouTube videos and extract their transcripts, batch retrieve structured subtitle data for media research, extract transcripts from trending YouTube content automatically.
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This skill helps users automatically extract YouTube video transcripts and metadata via the BrowserAct API. The Agent should proactively apply this skill when users express needs like extracting full transcript from a specific YouTube video, getting subtitles and metadata for video content analysis, gathering video titles and likes counts, summarizing YouTube videos without watching them, collecting channel details from a video URL, tracking transcript automation for specific videos, scraping YouTube subtitles for internal knowledge bases, fetching full video content for AI summarization pipelines, downloading structured transcripts from YouTube links, analyzing video text content for media research, monitoring video publisher information and channel links, or building datasets from YouTube video transcripts.
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Azure AI Transcription SDK for Python. Use for real-time and batch speech-to-text transcription with timestamps and diarization. Triggers: "transcription", "speech to text", "Azure AI Transcription", "TranscriptionClient".
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Azure AI Transcription SDK for Python. Use for real-time and batch speech-to-text transcription with timestamps and diarization. Triggers: "transcription", "speech to text", "Azure AI Transcription", "TranscriptionClient".
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Prepare an agent activity transcript for a GitHub pull request or issue. Extract relevant prompts, decisions, and results from local logs, remove sensitive material, and preview the edited record before separately authorized publication.
Transcribe audio files through OpenAI's transcription API and save the results as text or JSON. Select a model, specify the language, or use speaker labeling to distinguish who said each part of a recording.
Turn meeting transcripts into structured GBrain pages with decisions, assigned tasks, and verified quotes. Resolve speakers using evidence, split recordings containing separate meetings, and update related people and companies with links and dated meeting context.
Analyze meeting transcripts to understand speaking share, verbal habits, listening, and facilitation. Receive feedback grounded in specific quotes, practical suggestions for future meetings, and comparisons across periods when enough meeting data is available.
Download YouTube captions and thumbnails from a URL or video ID. Save transcripts as Markdown or SRT subtitle files, select or translate languages, add chapters, and use AI processing to label speakers.
Corrects ASR/STT transcription errors — homophones, garbled terms, person-name errors, mixed Chinese/English — with dictionary rules plus Claude's built-in AI, no external API key required. Use to clean meeting notes, lecture transcripts, interviews or speech-recognition text, even when the user only says "fix this transcript". Not for structuring a clean transcript into minutes (use meeting-minutes-taker).
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Clean a raw transcript before ingestion: punctuate, paragraph, label speakers, fix mistranscribed technical terms, and split long recordings by topic. Use this skill when the user drops an auto-generated transcript, subtitle file, podcast or lecture transcript, meeting recording text, or asks to process a video or audio source. Do NOT use for text that is already prose, for summarising a transcript, or for the ingestion itself.
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When you want to extract content from a video — YouTube, Loom, Vimeo, Riverside, Zoom recording, local MP4, X/IG video, anything yt-dlp supports. Three depth modes user picks per invocation — transcript (just words, fast/free), visual (transcript + ffmpeg frame extraction + Claude vision pass on key moments), multimodal (Gemini native video ingestion if $GEMINI_API_KEY set, else dense Claude vision). Uses local Whisper for transcription (MLX-Whisper on Apple Silicon, faster-whisper elsewhere), falls back to platform-provided transcripts when available (Loom, Riverside, YouTube auto-subs). Saves to ~/Documents/videos/<source>-<slug>-<date>/ and optionally captures summary to second-brain raw/ as call-/meeting-/note-. Triggers on "/watch-video <url>," "watch this video," "transcribe this loom," "analyze this video," "summarize this recording," "key moments from this," "what happened in this video." This skill replaces and broadens the prior youtube-transcript skill.
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Fetch transcripts from YouTube videos. Use when user says "get transcript", "fetch transcript", "YouTube transcript", "pull captions", "what does this video say", or pastes a YouTube URL and wants the text content extracted. Also use when user wants to research or summarize a YouTube video.
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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).
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Transform transcripts, conversations, or raw notes into shareable content — either multiple social posts OR a single long-form article. Learns style from user's published content. Handles transcripts of any length (tested up to 5500+ lines / 90+ min). Use when user: (1) provides transcript/conversation/recording and wants social content, (2) asks to "turn recording/notes into posts", (3) mentions generating content from recordings/notes, (4) needs multiple publishable pieces from single source, (5) asks to write an article from a transcript — e.g. "write an article/long-form", "organize into an article", "write up this session", "turn this talk into a blog post", "deep dive". Social mode outputs 2-15 ready-to-publish pieces. Article mode outputs a structured long-form piece (3000-10000+ chars) written section-by-section.
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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).
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Search meeting notes, summaries, and transcripts saved in Granola. Ask questions in everyday language to find past discussions, then read detailed transcripts when you need the exact wording of a conversation.
Transcribe audio with Azure AI Speech and obtain word timestamps, speaker labels, and language identification. Use the resulting transcript in OpenMontage subtitle workflows, with a compatible local transcription fallback when Azure is unavailable.
Search indexed YouTube and podcast transcripts for statements by topic or speaker. Read surrounding passages, collect quotes with timestamped source links, and identify transcript-based start and end points for potential clips.