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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Claude Code skill: turn long videos into social-ready clips. Auto-find funny moments, cut, reframe to 9:16 with face-tracking, and burn opus-style captions.
| Date | Stars |
|---|---|
| 2026-07-31 | 501 |
| 2026-08-04 | 508 |
| 2026-08-06 | 508 |
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# Clipify A [Claude Code](https://claude.com/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](https://github.com/openai/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](https://claude.com/claude-code) - `ffmpeg` with `libx264` (`brew install ffmpeg`) - [`whisper`](https://github.com/openai/whisper) (`pip install openai-whisper`) - Python 3 with `numpy` (`pip install numpy`) ## Install ```bash 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](LICENSE). Built by [Louise de Sadeleer](https://github.com/louisedesadeleer), Growth at [Tella](https://tella.tv).
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matched fp:5122adfc80751e2b, llm:Description: 'Claude Code skill: turn long videos into social-ready clips. Auto-find funny moments, cut, reframe to 9:16 with face-tracking, and burn opus-style captions.' Language: Python
matched fp:5122adfc80751e2b, llm:Description: 'Claude Code skill: turn long videos into social-ready clips. Auto-find funny moments, cut, reframe to 9:16 with face-tracking, and burn opus-style captions.' Language: Python
matched fp:5122adfc80751e2b, llm:Description: 'Claude Code skill: turn long videos into social-ready clips. Auto-find funny moments, cut, reframe to 9:16 with face-tracking, and burn opus-style captions.' Language: Python