Video Edit logo

Video Edit

Community
hoodini
video-edit

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.

Overview

Publisherhoodini
Repositoryai-agents-skills
Skill namevideo-edit
Stars
280
Forks
62
Bundled files
32
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 32 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by hoodini on GitHub. Read the source before you install it.

Installation

Install the Video Edit AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/hoodini/ai-agents-skills.git /tmp/ai-agents-skills
mkdir -p .claude/skills
cp -r /tmp/ai-agents-skills/skills/video-edit .claude/skills/video-edit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Video Edit in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Video Edit on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Video Edit is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.

bash
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 sourceffprobe for dimensions, fps, duration, audio.

  2. Scaffoldcd ~/Documents/yuv-projects/videos && npx hyperframes init <slug> --video <path> --non-interactive. Rename the copied video to source.mp4.

  3. Extract audioffmpeg -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.

  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.

  9. (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).

  10. 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
  11. 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).

  12. 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
  13. Lintnpx 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").

  14. Rendernpx 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

FilePurpose
transcript-editor/index.htmlInteractive browser editor — video preview, RTL editing, dictionary apply, optional WebLLM AI suggestions, saves transcript_review.txt
references/setup.mdPrerequisites + install commands for Node / Python / FFmpeg / faster-whisper
references/transcribe.pyfaster-whisper transcribe with CPU fallback + word timestamps
references/serve_review.pyLocal review server — auto-loads editor, blocks until user clicks Approve & Render, then writes transcript_review.txt and exits (signals the agent)
references/make_review.pyApply corrections + emit transcript_review.txt (file-mode fallback)
references/apply_review.pyParse edited review file, redistribute word timings, update transcript.json
references/gen_body.pyCaption-body generator (editorial + matrix in liquid-glass pills)
references/host-template.html16:9 host composition with liquid effects + transition wipe
references/host-template-vertical.html9:16 host (1080×1920) — TikTok / Reels / Shorts layout: blurred bg, centered 16:9 footage strip, captions below, brand chip top-right
references/gen_body_vertical.pyCaption-body generator tuned for vertical (centered pill, larger fonts, narrower max-width)
references/liquid-blobs.htmlFull-duration drifting blob layer
references/caption-parallax-outro.htmlBehind-subject caption template (English; clone for other languages)
references/corrections-hebrew.mdKnown Hebrew Whisper mishears
references/transcript-review-workflow.mdThe pause/approve step in detail

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Video Edit AI skill do?

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,...

Why use Video Edit on TypingMind?

Because you install it once and use it with any model. Video Edit is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Video Edit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hoodini/ai-agents-skills/tree/master/skills/video-edit. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Video Edit?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Video Edit?

As many as you like. As long as a model supports skills, you can use Video Edit with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Video Edit AI skill free?

It is published on GitHub by hoodini. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇