video-use README

The project's README from github.com/browser-use/video-use. As one markdown file: README.md.

video-use

video-use

Introducing video-use — edit videos with Claude Code. 100% open source.

Drop raw footage in a folder, chat with Claude Code, get final.mp4 back. Works for any content — talking heads, montages, tutorials, travel, interviews — without presets or menus.

Try video-use in Browser Use Cloud.

What it does

Setup prompt

Paste into Claude Code, Codex, Hermes, Openclaw, or any agent with shell access:

Set up https://github.com/browser-use/video-use for me.

Read install.md first to install this repo, wire up ffmpeg, register the skill with whichever agent you're running under, and set up the ElevenLabs API key — ask me to paste it when you need it. Then read SKILL.md for daily usage, and always read helpers/ because that's where the editing scripts live. After install, don't transcribe anything on your own — just tell me it's ready and wait for me to drop footage into a folder.

The agent handles the clone, dependencies, skill registration, and prompts you once for your ElevenLabs API key (grab one at elevenlabs.io/app/settings/api-keys).

Then point your agent at a folder of raw takes:

cd /path/to/your/videos
claude    # or codex, hermes, etc.

For always-on editing from your own VPS or Telegram, run the agent through Browser Use Box. Watch the 15-second demo.

And in the session:

edit these into a launch video

It inventories the sources, proposes a strategy, waits for your OK, then produces edit/final.mp4 next to your sources. All outputs live in <videos_dir>/edit/ — the skill directory stays clean.

Manual install

If you'd rather do it by hand:

# 1. Clone and symlink into your agent's skills directory
git clone https://github.com/browser-use/video-use ~/Developer/video-use
ln -sfn ~/Developer/video-use ~/.claude/skills/video-use        # Claude Code
# ln -sfn ~/Developer/video-use ~/.codex/skills/video-use       # Codex

# 2. Install deps
cd ~/Developer/video-use
uv sync                         # or: pip install -e .
brew install ffmpeg             # required
brew install yt-dlp             # optional, for downloading online sources

# 3. Add your ElevenLabs API key
cp .env.example .env
$EDITOR .env                    # ELEVENLABS_API_KEY=...

How it works

The LLM never watches the video. It reads it — through two layers that together give it everything it needs to cut with word-boundary precision.

timeline_view composite — filmstrip + speaker track + waveform + word labels + silence-gap cut candidates

Layer 1 — Audio transcript (always loaded). One ElevenLabs Scribe call per source gives word-level timestamps, speaker diarization, and audio events ((laughter), (applause), (sigh)). All takes pack into a single ~12KB takes_packed.md — the LLM's primary reading view.

## C0103  (duration: 43.0s, 8 phrases)
  [002.52-005.36] S0 Ninety percent of what a web agent does is completely wasted.
  [006.08-006.74] S0 We fixed this.

Layer 2 — Visual composite (on demand). timeline_view produces a filmstrip + waveform + word labels PNG for any time range. Called only at decision points — ambiguous pauses, retake comparisons, cut-point sanity checks.

Naive approach: 30,000 frames × 1,500 tokens = 45M tokens of noise. Video Use: 12KB text + a handful of PNGs.

Same idea as browser-use giving an LLM a structured DOM instead of a screenshot — but for video.

Pipeline

Transcribe ──> Pack ──> LLM Reasons ──> EDL ──> Render ──> Self-Eval
                                                              │
                                                              └─ issue? fix + re-render (max 3)

The self-eval loop runs timeline_view on the rendered output at every cut boundary — catches visual jumps, audio pops, hidden subtitles. You see the preview only after it passes.

Design principles

  1. Text + on-demand visuals. No frame-dumping. The transcript is the surface.
  2. Audio is primary, visuals follow. Cuts come from speech boundaries and silence gaps.
  3. Ask → confirm → execute → self-eval → persist. Never touch the cut without strategy approval.
  4. Zero assumptions about content type. Look, ask, then edit.
  5. 12 hard rules, artistic freedom elsewhere. Production-correctness is non-negotiable. Taste isn't.

See SKILL.md for the full production rules and editing craft.

video-use (browser-use/video-use)

Searching Database…

29.1K ·GitHub
Video-use
What is this?video-use is an open-source Claude Code skill that turns raw footage into a finished MP4 — it cuts filler words and dead space, color-grades clips, burns subtitles, and adds animations through conversation with a coding agent.
Is this a standalone video editor, or a skill for coding agents?It is not a standalone editor — it's a skill that a coding agent (Claude Code, Codex, Hermes, or Openclaw) runs with shell access to drive ffmpeg, transcription, and rendering on your machine.
How do I get it running?Paste the setup prompt into your coding agent; it clones the repo, installs dependencies, registers the skill, and asks once for your ElevenLabs API key, then you point it at a folder of raw takes.
Do I need an ElevenLabs API key?Yes — ElevenLabs Scribe provides the word-level transcription the whole pipeline relies on, plus you need a coding agent with shell access such as Claude Code, Codex, Hermes, or Openclaw.
Does the AI actually watch my footage?No — the AI never watches the video frame by frame. It reads a word-level transcript and only pulls up filmstrip/waveform images at ambiguous decision points, so purely visual cues without audio boundaries can be harder to catch.
What tools can generate the animation overlays?Overlays can be built with HyperFrames, Remotion, Manim, or PIL, with a separate sub-agent spawned in parallel for each animation.
Why does it read a transcript instead of scanning raw frames?Reading avoids feeding the model huge amounts of pixels — the README contrasts a naive frame-dump costing 45M tokens versus roughly 12KB of text plus a handful of PNGs for the same footage.
Does it check its own work before showing me the result?Yes — a self-eval loop runs a timeline check on the rendered output at every cut boundary, catching visual jumps or audio pops, and you only see the preview once it passes.
Can I run this as an always-on editing bot?Yes, you can run the agent through Browser Use Box for always-on editing from your own VPS or via Telegram.
What are similar projects?open-edit, autoclip, ffmpeg-skill, creator-flow, herk-edit-kit, Drift, guizang-product-video-skill, brag
Which topics is it in?transcription, vps, subtitles, video-editing, hermes-agent, audio-processing, speech-to-text, video-processing, telegram, openclaw, ffmpeg, skill
Creator
browser-use
Size
0.9 MB
Stars
29.1K
Updated
CommitOct 08
Age
6mo
Commits · 90 Days