Cut Video

A Claude Code skill that tightens long-form recordings — removes silences, ums, fillers, and dead air while preserving laughs and comedic pauses.

More accurate than cutting from Whisper alone. Cuts are driven by Montreal Forced Aligner (MFA) word boundaries (~10–20ms precision, with true inter-word silences as explicit intervals) rather than Whisper timestamps (±100–300ms, with pauses embedded inside word durations). Whisper is used only to produce the transcript text; MFA aligns it to the audio. The result: tighter cuts, no clipped word onsets/tails, and reliable silence detection.

Point it at any video file and it will:

1. Make a proxy — transcodes HEVC/4K → 1080p H.264 with hardware decode so every subsequent step runs in seconds, not minutes.
2. Transcribe with Whisper — tiny.en, for the transcript text only.
3. Force-align with MFA — snaps the transcript to the audio at ~10–20ms precision; this is the timing engine for every cut.
4. Plan the cuts — applies a tone-aware silence heuristic (aggressive / balanced / sentimental / documentary), strips fillers, drops false starts and retakes. Shows you the plan before touching the video.
5. Render — uses ffmpeg trim+concat (not select, which misaligns audio) for a clean cut.

Runs entirely on your machine. No cloud APIs. Built for Apple Silicon — fast.

Why this exists

Most silence-cutters (auto-editor, opus, etc.) are either too aggressive (they cut your laughs) or too slow (the wrong ffmpeg flags). This skill was tuned against a 7-minute 4K HEVC interview that took 5+ minutes to even decode in other tools. With the right flags (-hwaccel videotoolbox, -preset fast, hardware-friendly trim/concat) the same source cuts in ~30 seconds.

Specifically, this skill is opinionated about three things most tools get wrong:

• Laughs aren't silences. Before any silence cut, it checks audio amplitude. If a "silent" window has audible content, it stays.
• Comedic pauses are content. A 4-second pause after a punchline isn't dead air — it's the joke. The skill compresses dead air aggressively but preserves comedic timing.
• Audio sync matters. Most ffmpeg -vf select cut tutorials produce videos with drifting audio. This skill uses trim+concat, which keeps everything in sync.

Requirements

• macOS (uses VideoToolbox for hardware-accelerated decode — works on Linux/Windows if you remove -hwaccel videotoolbox flags)
• Claude Code
• ffmpeg with libx264 (brew install ffmpeg)
• whisper (pip install openai-whisper) — for the transcript text
• Montreal Forced Aligner — the alignment / timing engine. The skill installs this for you on first run (creates a mfa conda env and downloads the English model; installs miniforge via Homebrew if conda is missing). No manual setup needed. If Homebrew isn't available, the skill falls back to Whisper word timestamps + ffmpeg silencedetect (and says so in the plan).

Install

git clone https://github.com/louisedesadeleer/cut-video.git ~/.claude/skills/cut-video

Restart Claude Code and /cut-video is available as a slash command.

Usage

In Claude Code:

/cut-video

Then paste a video file path when asked. The skill will:

1. Make a 1080p H.264 proxy (skip if source is already small/H.264)
2. Transcribe the audio with Whisper (transcript text only)
3. Force-align the transcript to the audio with MFA — the timing source for every cut
4. Print a proposed cut list — timestamps, what gets cut, what stays, total compression
5. Wait for your "go"
6. Render the cleaned version and open it for review

Tone presets

When asked, pick one:

• aggressive (default) — tightest pacing; ~40–60% runtime reduction, mid-sentence cuts, fillers and hedges sliced
• balanced — aggressive on dead air, preserves comedic beats
• sentimental — gentler everywhere; longer pauses preserved for emotional moments
• documentary — only cuts genuine dead air; keeps thinking pauses intact

What this skill doesn't do

• It doesn't add zooms, layouts, b-roll, memes, captions, or motion graphics. Pair it with clipify for short-form output, or your editor of choice for the rest.

Built by @louisedesadeleer. PRs welcome.