Package an existing talking-head / interview / podcast video with timed, designed GRAPHIC OVERLAY cards — kinetic titles, lower-thirds, data callouts, quotes, side panels, picture-in-picture — synced to the transcript, on a 16:9 / 9:16 / 4:5 canvas of your choice; the clip plays untouched underneath

First, keep this skill fresh — confirm with the user before running: npx hyperframes skills update talking head recut . A fast no op when everything is current; otherwise it refreshes this skill plus the core domain skills it depends on before you rely on them. Talking Head Recut Talking Head Recut takes a local video that plays in full and layers a sequence of timed, designed graphic cards onto it — titles, lower thirds, data callouts, quotes, side panels, picture in picture — synced to what's being said. The agent designs the cards (timing + content) and writes each card's HTML directly in the conversation , then assembles a single composition HTML and renders it to MP4 via hyperframes . There is no fixed archetype list and no prescribed card structure — the overlays emerge from what the transcript actually says. The front door is /hyperframes . This skill packages an existing talking head clip with designed graphic cards (titles, lower thirds, data callouts, quotes, side panels, PiP) — not plain captions (the spoken words as text). The clip plays untouched. Any other intent — plain subtitles, a standalone graphic, a from scratch video — or any uncertainty → read /hyperframes first: the intent layer owns every route decision. Graphic packaging sibling of embedded captions . Captions add the spoken words as a readable subtitle; this adds designed graphics on top of the playing video. Plain subtitles → embedded captions . Build a video from scratch → the creation workflows ( product launch video / faceless explainer / …). Routed through /hyperframes , the intent layer confirms only the input (which clip) and announces the render strategy questions as deferred asks — aspect, layout, style group, and card count stay at Step 7, where the probed footage and transcript ground the recommendations; the layer's run shape questions don't apply. A BRIEF.md , when present, carries the confirmed input and any user notes — read it first. Inspectable intermediate files in the work directory: metadata.json — duration / width / height / fps audio.mp3 — extracted audio transcript.json — a flat word array [{ text, start, end }, …] (Whisper; no segments , no words wrapper) storyboard.json — lightweight card outline (the agent's plan) public/cards/card XX.html — one HTML fragment per card public/index.html — final assembled composition output.mp4 — rendered video CLI Resolution This skill runs entirely on the hyperframes CLI plus system ffmpeg / ffprobe . Transcription is local Whisper via hyperframes transcribe — no third party service, API key, or rate limited proxy. Workflow 1. Check Environment Required: ffmpeg / ffprobe (system) <SKILL DIR /assets/fonts/ .woff2 , <SKILL DIR /assets/vendor/gsap.min.js (bundled inside this skill, staged to work dir in Step 9) Transcription needs no key — hyperframes transcribe runs Whisper locally (Step 4). Strongly recommended on macOS for hyperframes render : 2. Create a Work Directory All artifacts live under videos/<project name / — the same convention as the other video workflows ( product launch video / faceless explainer / pr to video ). Keep the cwd at the workspace root; everything below writes under this one subdirectory. 3. Extract Audio and Metadata Outputs: metadata.json (read width / height / duration ; fps = the r frame rate fraction evaluated, e.g. 30000/1001 → 29.97 ) + audio.mp3 . 4. Transcribe Local Whisper — no API key, no proxy, no rate limit. Writes a word level transcript.json into the work dir (word text + start / end timestamps). Read it for the word / sentence timings that drive card timing in Step 6; group words into sentences yourself at punctuation / pauses if you need segment level chunks. Clamp to media duration. Whisper can return the final word's end a hair past the actual clip length — clamp every card endSec and composition.durationSeconds to the metadata.json duration, or the render will show a black tail past the video. 5. Correct Transcript transcript.json is a flat array of word objects — [{ "text": "...", "start": s, "end": s }, …] (no segments array, no words wrapper; the per word key is text ). Read it and fix obvious ASR errors: Homophones, product names, technical terms, punctuation Edit a word's text in place; preserve its start / end timestamps There is no pre grouped segments array — group words into sentences yourself (split at terminal punctuation / pauses) when you need segment level chunks for card timing 6. Draft a Lightweight Storyboard (in chat) No CLI involved. Read transcript.json + metadata.json and design cards directly. storyboard.json is an agent internal planning artifact — no CLI command consumes it; it exists so you can think clearly about timing and content before writing each card's HTML. Keep the shape consistent with the example below so the same outline can drive the composition you author in Step 9: Required Card fields: field type purpose id string stable id used in card HTML & GSAP selectors intent string natural language description; fed to card synthesis startSec / endSec number times in seconds (endSec startSec) accentIndex 0 \ 1 \ 2 \ 3 \ 4 which of the 5 theme accent colors this card pulls zone enum (see below) where on the canvas the card lives contentHints object free form bag; agent puts kicker/title/detail/data/quote here archetype (optional) string free form label you may attach to remember a card's pattern; absent = free form, which is the default transition (optional) enum: cut \ fade \ slide \ wipe declarative card to card transition Five zone values: zone resolved bounds when to use fullscreen covers whole canvas hero moments, big numbers, mantras whiteboard area inset 40px margin (or 45% of portrait height) dense data / annotated content lower third bottom 30% band annotation over visible video side panel right 42% (landscape) or bottom 40% (portrait) data side, video other side video overlay full canvas, expects mostly transparent card annotation overlays on full bleed video When you assemble the composition in Step 9, resolve each card's zone into pixel bounds on the card host wrapper following the table above. Video bounds are set once at composition level ( videoTrack.bounds ); to make video appear to "move between cards", author GSAP tweens against video wrap in the composition's <script (see Step 9). No prescribed card roles, no prescribed narrative arc. Cards emerge from what the video actually says — could be all quotes or all data, could open with a number or with a story. Let the transcript drive the rhythm. How many takeaways? — auto infer from duration + density. No fixed upper limit. Pick a base pace from the video duration, then adjust by information density . Only floor is fixed: minimum 5 cards so even short videos have rhythm. Step 1 — base pace by duration (the natural sec/card for medium density): video duration base pace (sec per card) rationale < 60s (short reel) 6–8s viewers expect fast cuts in short form 60s – 3 min 8–12s normal social pace 3 – 10 min 12–20s give breathing room; each card carries more 10 – 30 min 20–35s long form lecture / interview rhythm 30 min 30–60s episodic, near chapter feel Step 2 — density multiplier (multiplies the base pace): signal in the transcript multiplier effect High density — many numbers, distinct claims, staccato pacing, list like enumeration, every 1–2 sentences is a new idea × 0.7 cuts faster, more cards Medium density — mixed flow with both data and narrative × 1.0 base pace Low density — one extended story, repeated reframing, slow reflective pacing, single argument unfolding × 1.5 cuts slower, fewer cards Step 3 — compute: Examples (notice — no upper clamp ; long videos naturally produce more cards): 30s reel, single punchline (low density) → 7 × 1.5 = 10.5s/card → round(30/10.5)=3 → floor to 5 cards 60s reflective monologue (low density) → 10 × 1.5 = 15s/card → 4 → floor to 5 cards 121s talking head with rich data (high density) → 10 × 0.7 = 7s/card → 17 cards 5 min interview, mixed density → 16 × 1.0 = 16s/card → 19 cards 10 min deep dive, high density → 16 × 0.7 = 11s/card → 55 cards 30 min lecture, medium density → 28 × 1.0 = 28s/card → 64 cards 1 hr podcast, low density → 45 × 1.5 = 67.5s/card → 53 cards When a card holds longer than ~15s, plan for a richer card (data block, multi step reveal, several sub points unfolding with staggered animations) — a static one liner gets boring past 8s. For long pieces where many cards exceed 30s, consider chunking the timeline into sub compositions (one .html per chapter, mounted with data composition src ) so the GSAP timeline per file stays manageable — see the timeline track too dense HyperFrames lint warning. content can be a plain string ("Title: annualized 5.69%\nNotes: ...") or any JSON shape that captures the data. The agent decides the shape per card. Optional outro. This skill ships no fixed brand outro . If the user wants a closing card, design a neutral one yourself (wordmark + one line tagline, ~1.5 2s, fade in short hold fade out), append it to cards[] , and extend composition.durationSeconds to its endSec . Otherwise end on the last content card. 7. Decide Render Strategy Confirm Visual Direction with User (DO THIS FIRST) Before you start designing cards or deciding bounds, ask the user to pick the output ratio, the layout, the style, and the card density preset . Frames are auto selected from the chosen layout × style combination (see "Auto pick frame" table below). Before sending the question, precompute two things : 1. recommendedRatio from the source video's aspect ratio ( metadata.json width / height): sourceAspect = width / height sourceAspect ≥ 1.5 (≥ ~3:2 wide) → recommend 16:9 sourceAspect ≤ 0.7 (≤ ~9:13 tall) → recommend 9:16 0.7 < sourceAspect < 1.5 (near square) → recommend 4:5 Mark the recommended option'

talking-head-recut