Clipify

Find the funniest moments in a video, cut them as standalone clips, optionally reformat 16:9 → 9:16 (face-pan or split-screen), and burn opus-style word-by-word captions.

Inputs

• A video file path (the user will provide it; otherwise ask)
• Optional: requested format (9:16, 16:9, 1:1), if not given, ask after candidates are picked
• Optional: subtitle style preference, if not given, ask before captioning

Tooling (use only the fastest path)

• Whisper: whisper --model tiny.en --word_timestamps True --output_format json (≈10× faster than small.en; quality fine for English). For non-English: --model base (drop --language).
• ffmpeg: hardware decode is optional and platform-specific, use -hwaccel auto, or omit it (macOS: videotoolbox; Linux: vaapi/cuda/none). Add -preset ultrafast for renders. Use -c:v libx264 -crf 20 for the final master.
• Numpy for audio alignment (FFT cross-correlation). No scipy/cv2 needed.
• Scripts: <skill-dir>/scripts/ (where <skill-dir> is the directory containing this SKILL.md, typically ~/.claude/skills/clipify/)
  • analyze.py, speaker timeline from two ROI motion files
  • build_pan.py, ffmpeg crop x-expression with hard cuts
  • build_ass.py, opus-style ASS captions from whisper JSON
  • audio_align.py, find offset of a sub-clip in a longer source

Working dir: /tmp/clipify/ (mkdir at start, leave artifacts for debugging).

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Workflow

Step 1, Find the funniest parts

mkdir -p /tmp/clipify
ffmpeg -y -i "$VIDEO" -vn -ac 1 -ar 16000 /tmp/clipify/audio.wav
whisper /tmp/clipify/audio.wav --model tiny.en --word_timestamps True --output_format json --output_dir /tmp/clipify --language en

Read the resulting JSON (or .txt) and pick 3, 5 candidate clips. Funny signals to scan for: