browser-use/video-usePublic

Edit videos with coding agents

AI summary: Agentic skill that enables Claude Code to autonomously edit and render complex videos via natural language.

Stars
28K
+214 today
Forks
3.3K
Watchers
150
Open issues
28
Open PRs
116
Contributors
~8
Commits
22
Branches
5

PythonMITCreated Apr 12, 2026Last push 2d ago+848 stars this week+4.1K this month

Quick answers

What is video-use?
Agentic skill that enables Claude Code to autonomously edit and render complex videos via natural language.
What does video-use do?
video-use acts as an innovative interface that allows AI agents, specifically Claude Code, to perform complex video editing tasks autonomously. By dropping raw footage into a designated folder and providing a natural language prompt, the tool orchestrates the necessary underlying scripts (like FFmpeg) to trim, splice, and compile a final MP4 file. It entirely removes the need for manual timeline editing in heavy, complex GUI applications, replacing it with a text-driven workflow. The system handles everything from removing filler words and color grading to generating animation overlays and self-evaluating the render at every cut boundary.
Who is video-use for?
Content creators, marketers, and developers who want to dramatically accelerate their video production pipeline. It is perfect for those who prefer describing an edit conceptually rather than executing it manually on a timeline.
How popular is video-use on GitHub?
browser-use/video-use has 28,039 stars and 3,305 forks on GitHub, and gained 848 stars in the last 7 days.
What license does video-use use?
browser-use/video-use is released under the MIT license.

Star history

since Jul 28, 2026
010K20KJul 2026Aug 2026Sep 2026Oct 2026
28K stars as of Oct 4, 2026. Measured daily since Jul 28, 2026; GitHub no longer exposes earlier star timestamps.

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20 commits in the last yearLessMore

Signals and awards

derived from tracked data
  • Widely adopted

    28,039 stars

  • Permissive license

    MIT

  • Continuous integration

    Automated checks passing

  • Repeat trending

    10 trending appearances

What video-use does

video-use acts as an innovative interface that allows AI agents, specifically Claude Code, to perform complex video editing tasks autonomously. By dropping raw footage into a designated folder and providing a natural language prompt, the tool orchestrates the necessary underlying scripts (like FFmpeg) to trim, splice, and compile a final MP4 file. It entirely removes the need for manual timeline editing in heavy, complex GUI applications, replacing it with a text-driven workflow. The system handles everything from removing filler words and color grading to generating animation overlays and self-evaluating the render at every cut boundary.

Content creators, marketers, and developers who want to dramatically accelerate their video production pipeline. It is perfect for those who prefer describing an edit conceptually rather than executing it manually on a timeline.

  • Automated Trimming: Intelligently identifies and removes filler words, false starts, and dead space between takes.
  • Text-Driven Workflow: Operates entirely via natural language prompts without requiring the user to navigate complex editing menus.
  • Automated Color Grading: Applies consistent, high-quality color profiles across all segments using advanced FFmpeg chains.
  • Sub-Agent Animation: Spawns parallel agents to generate custom graphical overlays using tools like Remotion or Manim.
  • Cut-Boundary Evaluation: Self-evaluates the visual and audio fidelity at every edit point before presenting the final render.
  • Persistent Session Memory: Saves project state in a markdown file so editing sessions can be resumed days later seamlessly.

Where teams use it

Rapid Content Creation

Quickly turning raw talking-head footage into a polished, captioned social media clip with zero manual intervention.

Tutorial Compilation

Splicing together multiple screen recordings, adding smooth audio fades, and generating introductory title animations automatically.

Automated Quality Control

Reviewing hours of interview footage to extract key quotes and compile them into a seamless, color-graded highlight reel.

Headless Video Processing

Running the editing agent continuously on a VPS to process incoming video drops from a remote team automatically.

README

main branch

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

  • Cuts out filler words (umm, uh, false starts) and dead space between takes
  • Auto color grades every segment (warm cinematic, neutral punch, or any custom ffmpeg chain)
  • 30ms audio fades at every cut so you never hear a pop
  • Burns subtitles in your style — 2-word UPPERCASE chunks by default, fully customizable
  • Generates animation overlays via HyperFrames, Remotion, Manim, or PIL — spawned in parallel sub-agents, one per animation
  • Self-evaluates the rendered output at every cut boundary before showing you anything
  • Persists session memory in project.md so next week's session picks up where you left off

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.

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20 commits in the last 52 weeks.

When work happens

weekday and hour
SunMonTueWedThuFriSat036912151821Sun 0:00 — 0 commitsSun 1:00 — 0 commitsSun 2:00 — 0 commitsSun 3:00 — 0 commitsSun 4:00 — 0 commitsSun 5:00 — 0 commitsSun 6:00 — 0 commitsSun 7:00 — 0 commitsSun 8:00 — 0 commitsSun 9:00 — 0 commitsSun 10:00 — 0 commitsSun 11:00 — 2 commitsSun 12:00 — 1 commitsSun 13:00 — 0 commitsSun 14:00 — 0 commitsSun 15:00 — 1 commitsSun 16:00 — 1 commitsSun 17:00 — 0 commitsSun 18:00 — 0 commitsSun 19:00 — 0 commitsSun 20:00 — 0 commitsSun 21:00 — 1 commitsSun 22:00 — 0 commitsSun 23:00 — 0 commitsMon 0:00 — 1 commitsMon 1:00 — 0 commitsMon 2:00 — 0 commitsMon 3:00 — 0 commitsMon 4:00 — 0 commitsMon 5:00 — 0 commitsMon 6:00 — 0 commitsMon 7:00 — 0 commitsMon 8:00 — 0 commitsMon 9:00 — 0 commitsMon 10:00 — 0 commitsMon 11:00 — 1 commitsMon 12:00 — 0 commitsMon 13:00 — 0 commitsMon 14:00 — 0 commitsMon 15:00 — 0 commitsMon 16:00 — 0 commitsMon 17:00 — 0 commitsMon 18:00 — 0 commitsMon 19:00 — 0 commitsMon 20:00 — 0 commitsMon 21:00 — 0 commitsMon 22:00 — 0 commitsMon 23:00 — 0 commitsTue 0:00 — 0 commitsTue 1:00 — 0 commitsTue 2:00 — 0 commitsTue 3:00 — 0 commitsTue 4:00 — 0 commitsTue 5:00 — 0 commitsTue 6:00 — 0 commitsTue 7:00 — 0 commitsTue 8:00 — 0 commitsTue 9:00 — 0 commitsTue 10:00 — 0 commitsTue 11:00 — 0 commitsTue 12:00 — 0 commitsTue 13:00 — 0 commitsTue 14:00 — 0 commitsTue 15:00 — 0 commitsTue 16:00 — 0 commitsTue 17:00 — 0 commitsTue 18:00 — 0 commitsTue 19:00 — 0 commitsTue 20:00 — 0 commitsTue 21:00 — 0 commitsTue 22:00 — 0 commitsTue 23:00 — 0 commitsWed 0:00 — 0 commitsWed 1:00 — 0 commitsWed 2:00 — 0 commitsWed 3:00 — 0 commitsWed 4:00 — 0 commitsWed 5:00 — 0 commitsWed 6:00 — 0 commitsWed 7:00 — 0 commitsWed 8:00 — 0 commitsWed 9:00 — 0 commitsWed 10:00 — 0 commitsWed 11:00 — 0 commitsWed 12:00 — 0 commitsWed 13:00 — 0 commitsWed 14:00 — 0 commitsWed 15:00 — 2 commitsWed 16:00 — 1 commitsWed 17:00 — 0 commitsWed 18:00 — 0 commitsWed 19:00 — 0 commitsWed 20:00 — 0 commitsWed 21:00 — 1 commitsWed 22:00 — 0 commitsWed 23:00 — 0 commitsThu 0:00 — 0 commitsThu 1:00 — 0 commitsThu 2:00 — 0 commitsThu 3:00 — 0 commitsThu 4:00 — 0 commitsThu 5:00 — 0 commitsThu 6:00 — 0 commitsThu 7:00 — 0 commitsThu 8:00 — 0 commitsThu 9:00 — 1 commitsThu 10:00 — 0 commitsThu 11:00 — 0 commitsThu 12:00 — 0 commitsThu 13:00 — 0 commitsThu 14:00 — 0 commitsThu 15:00 — 0 commitsThu 16:00 — 0 commitsThu 17:00 — 0 commitsThu 18:00 — 0 commitsThu 19:00 — 0 commitsThu 20:00 — 0 commitsThu 21:00 — 0 commitsThu 22:00 — 0 commitsThu 23:00 — 2 commitsFri 0:00 — 0 commitsFri 1:00 — 0 commitsFri 2:00 — 0 commitsFri 3:00 — 1 commitsFri 4:00 — 0 commitsFri 5:00 — 0 commitsFri 6:00 — 0 commitsFri 7:00 — 0 commitsFri 8:00 — 0 commitsFri 9:00 — 0 commitsFri 10:00 — 0 commitsFri 11:00 — 0 commitsFri 12:00 — 0 commitsFri 13:00 — 0 commitsFri 14:00 — 0 commitsFri 15:00 — 0 commitsFri 16:00 — 0 commitsFri 17:00 — 0 commitsFri 18:00 — 0 commitsFri 19:00 — 0 commitsFri 20:00 — 0 commitsFri 21:00 — 0 commitsFri 22:00 — 0 commitsFri 23:00 — 0 commitsSat 0:00 — 0 commitsSat 1:00 — 0 commitsSat 2:00 — 0 commitsSat 3:00 — 0 commitsSat 4:00 — 0 commitsSat 5:00 — 0 commitsSat 6:00 — 0 commitsSat 7:00 — 0 commitsSat 8:00 — 0 commitsSat 9:00 — 0 commitsSat 10:00 — 0 commitsSat 11:00 — 0 commitsSat 12:00 — 0 commitsSat 13:00 — 0 commitsSat 14:00 — 0 commitsSat 15:00 — 0 commitsSat 16:00 — 0 commitsSat 17:00 — 0 commitsSat 18:00 — 2 commitsSat 19:00 — 2 commitsSat 20:00 — 0 commitsSat 21:00 — 0 commitsSat 22:00 — 0 commitsSat 23:00 — 0 commits
Commit volume by weekday and hour (UTC). Larger dots mean more commits.
DateListRankStars gained
Sep 24, 2026daily#14+191
Sep 23, 2026daily#14+191
Sep 22, 2026daily#7+155
Sep 3, 2026daily#8+472
Sep 2, 2026daily#8+472
Sep 1, 2026daily#9+591
Aug 6, 2026daily#13+320
Aug 5, 2026daily#13+320
Jul 3, 2026daily#18+11
Jun 29, 2026daily#25+10