Talking Head Recut

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

# hyperframes — transcription (local Whisper) + rendering the assembled HTML to MP4
npx hyperframes --help

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

npx hyperframes doctor          # ffmpeg, headless browser, render deps
# confirm bundled assets:
ls "<SKILL_DIR>/assets/fonts" "<SKILL_DIR>/assets/vendor/gsap.min.js"

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:

export PRODUCER_BROWSER_GPU_MODE=hardware

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.

VIDEO_PATH="/absolute/path/input.mp4"
WORK_DIR="videos/$(basename "$VIDEO_PATH" | sed 's/\.[^.]*$//')"
mkdir -p "$WORK_DIR"

3. Extract Audio and Metadata

# metadata — duration / width / height / fps
ffprobe -v error -select_streams v:0 \
  -show_entries stream=width,height,r_frame_rate \
  -show_entries format=duration -of json "$VIDEO_PATH" > "$WORK_DIR/metadata.json"
# audio
ffmpeg -y -i "$VIDEO_PATH" -vn -acodec libmp3lame -q:a 2 "$WORK_DIR/audio.mp3"

Outputs: metadata.json (read width/height/duration; fps = the r_frame_rate fraction evaluated, e.g. 30000/1001 → 29.97) + audio.mp3.

4. Transcribe

npx hyperframes transcribe "$WORK_DIR/audio.mp3" -d "$WORK_DIR" --json --model small.en

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:

{
  "schemaVersion": 3,
  "composition": {
    "fps": 30,
    "width": 1080,
    "height": 1920,
    "durationSeconds": 121.2,
    "layout": "portrait",
    "themeId": "noir",
    "seed": 42
  },
  "videoTrack": {
    "sourcePath": "input-video.mp4",
    "startSec": 0,
    "endSec": 121.2,
    "bounds": { "x": 0, "y": 0, "width": 1080, "height": 1920 }
  },
  "subtitles": { "enabled": false },
  "cards": [
    {
      "id": "card-01",
      "intent": "Hook with the speaker's anxious midnight question",
      "startSec": 0.5,
      "endSec": 13.0,
      "accentIndex": 0,
      "zone": "fullscreen",
      "contentHints": {
        "kicker": "AN HONEST QUESTION",
        "title": "The soul-searching question at 11 PM",
        "detail": "Client's 60-second voice message: 'If the RMB appreciates, does that mean my USD policy is a terrible loss?'"
      }
    }
  ]
}

Required Card fields: