/video-edit

name: video-edit
description: Edit any video into a captioned showcase — transcribe (any language, defaults to large-v3), present a transcript_review.txt for the user to fix mishears BEFORE rendering, then build a HyperFrames composition with liquid-glass caption pills, liquid blob background, liquid morph wipes, optional behind-subject text via background removal, and render the final video. Use whenever the user provides a video file and asks to edit it, caption it, add subtitles, fix existing captions, make a reel/promo/captioned tutorial, or "do the same" pattern as a prior captioned video. Supports English, Hebrew, and any Whisper-supported language. **Renders both 16:9 (YouTube / horizontal) and 9:16 (TikTok / Instagram Reels / YouTube Shorts) from the SAME 16:9 source** — vertical mode uses a centered footage strip with a blurred backdrop + liquid blobs and a vertical-tuned caption pill, no need to re-shoot. THE PIPELINE PAUSES FOR USER APPROVAL on the transcript before final render — this is the support mechanism for getting captions perfect (especially Hebrew). Pairs with hyperframes, hyperframes-cli, hyperframes-registry, and yuv-design-system skills.

Video Edit — Captioned Showcase Pipeline

End-to-end captioned video editor on top of HyperFrames. The user gives you a video; you orchestrate transcribe → review → render and ALWAYS pause for transcript approval before the long render.

Where this skill sits in the YUV.AI pyramid

`video-edit` is in the **middle tier** of the YUV.AI skills pyramid alongside `yuv-design-system`, `yuv-decks`, `yuv-viral-video`, `parallax-landing-page`, and `video-to-landing-page`. The top-tier orchestrator `yuv-pilot` routes here whenever the user wants a captioned showcase, tutorial, or talking-head edit with subtitles.

This is the more general video sibling to `yuv-viral-video`. The split:

• `yuv-viral-video` — opinionated YUV.AI viral-short pipeline (MrBeast pacing, signature editorial style)
• `video-edit` — general captioned editor with transcript-review-before-render (Hebrew + English + any Whisper language)

For YUV.AI-branded captioned video, pair this skill with `yuv-design-system` (Neon mode for type/palette decisions). For generic / third-party captioned video, this skill works standalone.

When to invoke

• A path to a video file (mp4/mov/mkv) + a request to "edit", "caption", "add subtitles", "make a reel/promo", "do the same"
• "Fix the captions / Hebrew misspells" — re-enter at the review step on an existing project
• Any captioned tutorial / talking-head / promo build

Save location

**Default:** `~/Documents/yuv-projects/videos/<slug>/` — always save captioned video projects here so renders are findable. The `<slug>` is short, derived from the topic or source filename.

mkdir -p ~/Documents/yuv-projects/videos
cd ~/Documents/yuv-projects/videos
# Initialize the project here.

Final render lands at `~/Documents/yuv-projects/videos/<slug>/renders/<name>_FINAL.mp4`. Tell the user where the video lives at the end of the render.

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Workflow (12 steps)

1. **Probe the source** — `ffprobe` for dimensions, fps, duration, audio. 2. **Scaffold** — `cd ~/Documents/yuv-projects/videos && npx hyperframes init <slug> --video <path> --non-interactive`. Rename the copied video to `source.mp4`. 3. **Extract audio** — `ffmpeg -i source.mp4 -vn -ac 1 -ar 16000 audio.wav`. 4. **Transcribe** — copy `references/transcribe.py` into the project. Default model `large-v3` (best Hebrew). CUDA usually fails on Windows (missing cuDNN); the script falls back to CPU int8. Force `language="he"` for Hebrew, `language="en"` for English; otherwise auto-detect. 5. **Apply known corrections** — copy `references/corrections-hebrew.md` content into a `corrections.json` at the project root (keys = wrong token, values = correct token). 6. 🛑 **STOP — start the review server and let the user approve in a webapp.** First apply known corrections: copy `references/make_review.py` into the project and run `python make_review.py`. It applies `corrections.json` to `transcript.json`.

Then spawn the review server **as a background task** (it blocks until the user clicks "Approve & Render" in the browser):

   python "$HOME/.claude/skills/video-edit/references/serve_review.py" .
   # On Windows: python "C:\Users\<you>\.claude\skills\video-edit\references\serve_review.py" .

The server prints a line like `REVIEW_URL=http://localhost:PORT/`. Grab that URL from the background-task output (or read stdout) and send the user:

> 👉 Review your transcript here: **http://localhost:PORT/** > When you click **Approve & Render**, I'll continue automatically.

The agent **does not need a "continue" message** — when the user clicks the button, the server writes `transcript_review.txt` to the project dir AND exits with code 0. The agent's background-task notification fires, and the pipeline resumes from step 8.

**Fallback if no browser / no server**: open the editor as a static file (`start "" "$HOME/.claude/skills/video-edit/transcript-editor/index.html"`), ask the user to pick the project folder, edit, save `transcript_review.txt` back into the project, and reply "continue". The editor supports both modes.

7. **(Optional) Background removal** — see step 7 below; can run in parallel with the user's review.

8. After approval, run `python references/apply_review.py`. It re-tokenises edited lines and redistributes word timings back into `transcript.json` so caption sync still works. 7. **(Optional) Background removal** — if any talking-head segment needs behind-subject text, extract the segment as `outro.mp4` (or `intro.mp4`) and run `npx hyperframes remove-background <clip>.mp4 -o <name>_subject.webm --quality best`. CPU only on most setups (~3–8 min for a ~15s 1440p clip). 8. **Re-encode source with dense keyframes** — multi-worker render seeks freeze on sparse keyframes. Always run: