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

/watch video — Transcribe and analyze any video at the depth you choose Replaces and broadens the prior youtube transcript skill. YouTube is now one of many sources; depth is user controlled. Step 1 — Parse input Accept: YouTube : full URL, youtu.be/<id , youtube.com/shorts/<id , raw 11 char ID Loom : loom.com/share/<id or loom.com/embed/<id Vimeo : vimeo.com/<id Riverside : download URL or local file Zoom : local .mp4 from a downloaded recording X / IG / TikTok video : URL — defers to social fetch for metadata, uses yt dlp for the file Local file : any path to an .mp4 / .mov / .webm / .mkv Detect source from URL pattern or file extension. If ambiguous, ask. Step 2 — Parse depth mode Invocation Mode What you get /watch video <url transcript (default) Clean text, metadata, optional chapters /watch video <url transcript transcript Same as default /watch video <url visual visual Transcript + frames at intervals + Claude vision pass identifying key moments /watch video <url multimodal multimodal Native video to Gemini (if $GEMINI API KEY ), else dense Claude vision frame by frame If the depth isn't specified and the video is 10 minutes, ask before defaulting (visual/multimodal cost real money on long videos). Step 3 — Pull metadata For URL sources, use yt dlp: Capture: title, uploader/channel, duration, upload date, description (first paragraph), chapters (JSON or null). For local files, use ffprobe: Step 4 — Build workdir Where: source : youtube / loom / vimeo / riverside / zoom / local slug : kebab case of title (first 4–6 words, max 50 chars) date : YYYY MM DD Step 5 — Get the transcript Backend selection (in order): 1. Platform provided transcript if it exists and looks complete: YouTube: yt dlp write sub write auto sub skip download sub lang en sub format vtt Loom: fetch via https://www.loom.com/share/<id page metadata or Loom API if $LOOM API KEY set Riverside: built in transcripts available on the recording's share page If platform transcript exists and has timestamps, use it. Skip Whisper. 2. MLX Whisper local (default fallback — fast on Mac M series): Or via the CLI: mlx whisper <file model mlx community/whisper large v3 turbo output dir <workdir 3. whisper.cpp (further fallback if MLX unavailable) Download the video file first if it's a URL (use yt dlp; Loom/Vimeo/YT all supported): 720p is plenty for transcription and frame analysis (smaller download, faster processing). Clean the transcript (only needed for YouTube auto subs which have rolling captions; Whisper output is already clean): Save final to <workdir /transcript.txt . Step 6 — If transcript mode: stop here Output: transcript.txt metadata.json One line summary in chat: title, source, duration, word count Path to workdir (Optional) Step 9 — offer to capture to second brain Step 7 — If visual mode: extract frames + vision pass Frame extraction (ffmpeg) Cadence by source heuristic: Source type Frame cadence Screen share / Loom / demo 1 frame per 5s (UI changes fast) Talking head / podcast 1 frame per 30s (slow change) Slide presentation 1 frame per 10s + force a frame on each detected scene change Default if unsure 1 frame per 15s For scene change detection (slide decks especially): Vision pass Pair each frame with the transcript chunk for the same timestamp window. Then batch send to Claude vision for synthesis. Per frame batch prompt (up to ~10 frames per call): Here are N frames from a video at timestamps T1..TN. For each frame, describe what's on screen in 1–2 sentences. Flag: (a) UI changes from previous frame, (b) text visible on screen, (c) any moment that looks like a decision, action, or notable event. Also note the transcript text spoken during this window. Save the output as <workdir /moments.md : Generate summary After moments are identified, synthesize the whole video into <workdir /summary.md : Step 8 — If multimodal mode Backend selection 1. Gemini native if $GEMINI API KEY is set (much cheaper + faster than per frame for long videos): Default model: gemini 3.5 flash (released May 2026, ~$1.50 input / $9 output per 1M tokens; ~$0.15/sec of video; beats 3.1 Pro on coding/agentic benchmarks at 4× the speed). Override to gemini 3.1 pro for brand audits / high stakes analysis where details matter; gemini 2.5 flash lite for bulk cheap processing. Files persist in Gemini Files API for ~48 hours — useful for re querying the same video with different prompts. 2. Dense Claude vision fallback if no Gemini key: Frame cadence: 1 frame per 3s (much denser than visual mode) Batch through Claude vision with the multimodal analysis prompt Slower and more expensive than Gemini for long videos — warn the user before running on 10min content Multimodal output Same summary.md template as Step 7 + an extended section: Exact extra sections depend on the use case (brand audit, ad review, talk delivery review, client call read). Use case is inferred from the source + the user's verbal framing when invoking. Step 9 — Optional: capture to second brain After any mode completes, offer: "Want to capture this to second brain? I'll write a call <slug .md (or meeting / note / resource ) to ${SECOND BRAIN VAULT: $HOME/Documents/SecondBrain}/raw/ with the summary, source URL, and transcript link." Type prefix by source: Source Prefix Loom / Zoom / Riverside / Otter / call recording call Meeting (own notes, not a transcript) meeting Talk / keynote / conference note Ad / landing page video / marketing reference / competitor video resource File body: 1 line source, the summary, link to full workdir. Step 10 — Report In chat: One line headline: <source · <title · <duration · <mode · <word count words Workdir path For visual / multimodal : brief list of top 3 key moments For all modes: any action items / decisions flagged for triage If captured to second brain: that path too Sources reference Source Download Built in transcript Notes YouTube yt dlp Auto subs ( write auto sub ) Same as the prior youtube transcript skill Loom yt dlp (Loom supported) Yes — fetch via embed metadata or Loom API Async screenshare focus — prime use case Vimeo yt dlp Sometimes Marketing/embed videos Riverside Direct URL from export, or local file Yes — Riverside generates them Podcast episodes Zoom Local .mp4 (downloaded recordings) Sometimes (Zoom audio transcript file) Client calls X / IG / TikTok Defer to social fetch for metadata, yt dlp for file No Short form Local file n/a n/a Drop a path Composes with social fetch — for X/IG/TikTok URL metadata (engagement, author, replies) before video processing second brain — capture summary as raw/call <slug .md , meeting , note , or resource per source type decide — when a video contains a flagged decision, route to /decide for structured capture pm — action items flagged in summary can be triaged to project boards slide deck — talk recordings → outline extraction → deck draft (loop) jab hook — quotes + clip worthy moments from podcast/talk videos feed BIP/promo posts skillify from video — primary use case for visual mode on process recordings. the user records themselves doing a workflow (Loom/screen share), this skill extracts transcript + key visual moments, then skillify synthesizes the workflow into a SKILL.md. "Record once, AI converts to skill." Error handling Failure Response Video unavailable / private / region locked Report and stop No subtitles + Whisper not installed Tell the user: pip install mlx whisper (Mac) ffmpeg missing (for visual/multimodal) Tell the user: brew install ffmpeg Vision pass returns empty / unclear Lower the frame count, retry, or fall back to transcript only with a note Multimodal requested but no $GEMINI API KEY and 30min video Warn cost, offer to fall back to visual mode yt dlp binary missing brew install yt dlp Notes on quality User picks depth, not the skill. Transcript / visual / multimodal are 3 different cost + latency profiles. Long videos ( 10 min) always confirm before spending on visual/multimodal. Platform transcript first, Whisper second. YouTube auto subs, Loom transcripts, Riverside built in transcripts — all free + instant when they exist. Fall back to MLX Whisper local only when nothing platform provided works. MLX Whisper local is the fast path on Mac. M series machines transcribe faster than real time. Cloud Whisper is a distant second choice — costs money, network dependency, worse latency on typical durations. Frame cadence by source type. Screen share / demos need 1 frame per 5s (UI changes fast); talking head podcasts need 1 per 30s (slow change). Default 15s if unsure. Wrong cadence = missed key moments OR wasted vision pass cost. 720p is plenty. Downloading 1080p / 4K for transcription + frame analysis wastes bandwidth + storage. yt dlp f "bv [height<=720]+ba/b[height<=720]" is the default. Scene change detection catches slide transitions. When the video is a slide presentation, add ffmpeg vf "select='gt(scene,0.3)'" to force a frame on each detected slide change — more reliable than pure time based sampling. Multimodal cost warning is non optional. Gemini multimodal on a 60 min video is meaningfully expensive. Warn before running; offer transcript only as fallback if the user isn't sure. Summary format includes routing hints. Decisions flagged + Action items flagged sections signal /decide and /pm follow ups. Downstream composability lives in the summary structure.

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