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- 310 registry:skill items, 1528 files, 7 categories (hub tags) - registry.json generated by @kortix/registry buildRegistry - nested SKILL.md in sn-da-* renamed SUBSKILL.md (no phantom skills) - provenance + licenses: SOURCES.md
84 lines
3.2 KiB
Markdown
84 lines
3.2 KiB
Markdown
# Clipify
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A [Claude Code](https://claude.com/claude-code) skill that turns long videos into social-ready clips.
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Point it at any video file and it will:
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1. **Find clip-worthy segments** — transcribes the video with [Whisper](https://github.com/openai/whisper) and scans the transcript for punchlines, reversals, awkward pauses, and audio peaks to propose 3–5 candidates.
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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).
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3. **Add subtitles** — burns opus-style word-by-word captions (big bold white, yellow active-word highlight).
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No cloud APIs. Runs entirely on your machine. No OpenCV. ~20s of work for a 20s clip on Apple Silicon.
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## Why this exists
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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).
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## Requirements
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- macOS (uses VideoToolbox for hardware-accelerated decode — works on Linux/Windows if you remove `-hwaccel videotoolbox` flags)
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- [Claude Code](https://claude.com/claude-code)
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- `ffmpeg` with `libx264` (`brew install ffmpeg`)
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- [`whisper`](https://github.com/openai/whisper) (`pip install openai-whisper`)
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- Python 3 with `numpy` (`pip install numpy`)
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## Install
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```bash
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git clone https://github.com/louisedesadeleer/clipify.git ~/.claude/skills/clipify
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```
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That's it. Restart Claude Code and `/clipify` is available as a slash command.
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## Usage
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In Claude Code:
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```
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/clipify
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```
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Then paste a video file path when asked. The skill will:
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1. Transcribe → propose 3–5 funny candidate clips with timestamps and titles
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2. Ask which to cut
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3. Ask 9:16 / 16:9 / 1:1
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4. If 9:16 from 16:9 with two faces: ask pan vs split-screen
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5. Ask subtitle style (opus / karaoke / minimal — or paste a reference image to match)
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6. Render and open the result
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Final clips land in `<source-video-dir>/clipify_out/`.
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## How the face-pan works
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No face detection model. Camera is static within a single clip, so:
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1. Eyeball each face's mouth+chin area as a rectangle on one sample frame.
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2. ffmpeg computes per-frame motion energy in each rectangle using frame differencing.
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3. Whichever rectangle has more motion at a given moment = that's the speaker.
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4. Build a hard-cut x-coordinate expression from the speaker timeline.
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5. Crop a vertical strip from the source that follows whoever's talking.
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Total cost: a few seconds of ffmpeg per clip. Works surprisingly well.
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## Repo structure
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```
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clipify/
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├── SKILL.md # the skill prompt Claude Code reads
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├── scripts/
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│ ├── analyze.py # speaker timeline from two ROI motion files
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│ ├── build_pan.py # ffmpeg crop x-expression with hard cuts
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│ ├── build_ass.py # opus/karaoke/minimal ASS captions from whisper JSON
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│ └── audio_align.py # find offset of a sub-clip in a longer source
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└── README.md
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```
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## License
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MIT — see [LICENSE](LICENSE).
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Built by [Louise de Sadeleer](https://github.com/louisedesadeleer), Growth at [Tella](https://tella.tv).
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