hoodini/ai-agents-skills/video-edit

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.

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): ``bash 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.

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

review.

1. 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.

1. (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).
2. Re-encode source with dense keyframes — multi-worker render seeks freeze on sparse keyframes. Always run:

``bash ffmpeg -y -i source.mp4 -c:v libx264 -preset medium -crf 18 -r 30 -g 30 -keyint_min 30 -sc_threshold 0 -pix_fmt yuv420p -movflags +faststart -c:a copy footage.mp4 ``

1. Re-load the (edited) transcript and generate the body sub-composition via references/gen_body.py. The generator emits the full compositions/components/caption-body.html with editorial + matrix alternating in liquid-glass pills, anchored lower-left-of-centre (clears bottom-right webcam PiPs).
2. Wire the host `index.html` from references/host-template.html. Layer order (z-index, NOT track-index):

• z0: footage .cam-bg
• z1: liquid blob background (compositions/liquid-blobs.html, mix-blend-mode: screen, full duration)
• z2: parallax behind-subject caption (intro and/or outro, when bg-removal used)
• z3: subject cut-out .cam-out / .cam-sub (with matching data-media-start)
• z6: body captions
• z46: progress bar + flash + liquid morph wipe

1. Lint — npx hyperframes lint. Must be 0 errors. Common fixes: GSAP/CSS transform conflict on the wipe element (use xPercent/yPercent or remove the CSS transform); overlapping tweens on the same property (add overwrite: "auto").
2. Render — npx hyperframes render --quality standard --fps 30 --output renders/<name>_FINAL.mp4. Standard is the right delivery target — high roughly doubles render time. Verify with 6–8 spot-check frames from across the timeline before reporting done.

Vertical (9:16) output for TikTok / Reels / Shorts

When the user asks for vertical / portrait / TikTok / Reels / 9:16 output (from a 16:9 source):

1. Clone the project to a sibling folder: cp -r project/ project-vertical/.
2. Replace its index.html with references/host-template-vertical.html (1080×1920 canvas, blurred-bg backdrop with liquid blobs, the 16:9 footage as a centered horizontal strip, captions below).
3. Replace its gen_body.py with references/gen_body_vertical.py (centered pill, larger fonts, narrower max-width), then re-run it to emit compositions/components/caption-body.html.
4. Drop the behind-subject cut-out + parallax sub-compositions (the cutout is aligned for 16:9; not worth re-aligning for v1). The vertical comp uses the blurred-source backdrop + blobs for atmosphere instead.
5. Update data-duration to the actual video duration. Update the brand-chip text in index.html (YUV.AI by default).
6. Lint + render — same commands. Output is 1080×1920. Drop straight onto TikTok / IG Reels / YT Shorts.

To deliver both 16:9 and 9:16 in one go, run two render commands (in parallel projects). The transcript_review.txt approval applies to both — same captions, two compositions.

Critical rules

• Never render the final without explicit transcript approval. The review step is the whole point.
• For Hebrew: large-v3 + language="he" + direction: rtl + Rubik (700 + 900 for editorial dual-weight emphasis).
• Caption pills always need an opaque dark backing — bare light text vanishes on white app UI.
• Centre caption pills horizontally but shift the centre x-coord left (e.g. left: 720px) when the footage has a bottom-right webcam PiP.
• The behind-subject cut-out clip MUST carry data-media-start matching its data-start (or matching the offset from the source if the clip was extracted), or the cut-out plays from frame 0 and desyncs.
• The remove-background webm keeps the original RGB and writes only the alpha mask — ffprobe reports yuv420p, which looks like "no alpha". Confirm via TAG:ALPHA_MODE=1 or composite over a solid colour.
• Outro/end cards with burned-in text — do NOT caption over them; they collide.

File references