manus-skills/.kortix/opencode/skills/media/clipify/README.md

Clipify

A Claude Code skill that turns long videos into social-ready clips.

Point it at any video file and it will:

1. Find clip-worthy segments — transcribes the video with Whisper and scans the transcript for punchlines, reversals, awkward pauses, and audio peaks to propose 3–5 candidates.
2. Create a 9:16 clip — cuts your chosen moment, then reframes 16:9 → 9:16 with hard-cut pans that follow whoever is speaking (or split-screen if you'd rather see both faces).
3. Add subtitles — burns opus-style word-by-word captions (big bold white, yellow active-word highlight).

No cloud APIs. Runs entirely on your machine. No OpenCV. ~20s of work for a 20s clip on Apple Silicon.

Why this exists

Most "auto-clip" tools are either expensive SaaS, slow, or produce slop. This skill is what I actually use to clip my long-form videos for LinkedIn and TikTok. Built for talking-head dialogue (interviews, podcasts, two-person setups).

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)
• Python 3 with numpy (pip install numpy)

Install

git clone https://github.com/louisedesadeleer/clipify.git ~/.claude/skills/clipify

That's it. Restart Claude Code and /clipify is available as a slash command.

Usage

In Claude Code:

/clipify

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

1. Transcribe → propose 3–5 funny candidate clips with timestamps and titles
2. Ask which to cut
3. Ask 9:16 / 16:9 / 1:1
4. If 9:16 from 16:9 with two faces: ask pan vs split-screen
5. Ask subtitle style (opus / karaoke / minimal — or paste a reference image to match)
6. Render and open the result

Final clips land in <source-video-dir>/clipify_out/.

How the face-pan works

No face detection model. Camera is static within a single clip, so:

1. Eyeball each face's mouth+chin area as a rectangle on one sample frame.
2. ffmpeg computes per-frame motion energy in each rectangle using frame differencing.
3. Whichever rectangle has more motion at a given moment = that's the speaker.
4. Build a hard-cut x-coordinate expression from the speaker timeline.
5. Crop a vertical strip from the source that follows whoever's talking.

Total cost: a few seconds of ffmpeg per clip. Works surprisingly well.

Repo structure

clipify/
├── SKILL.md           # the skill prompt Claude Code reads
├── scripts/
│   ├── analyze.py     # speaker timeline from two ROI motion files
│   ├── build_pan.py   # ffmpeg crop x-expression with hard cuts
│   ├── build_ass.py   # opus/karaoke/minimal ASS captions from whisper JSON
│   └── audio_align.py # find offset of a sub-clip in a longer source
└── README.md

License

MIT — see LICENSE.

Built by Louise de Sadeleer, Growth at Tella.