/clipify

name: clipify
description: Find compelling moments in a video — funny dialogue OR repeated impact actions like axe chops, hits, throws, drumbeats — cut them as standalone clips, optionally reformat 16:9 ↔ 9:16, time-warp pans for tighter reveals, and burn opus-style word-by-word captions. Use when the user mentions "clipify," "cut clips from this video," "make shorts from this," "find funny moments," "action montage," "cut on each chop/hit/punch," "reframe to 9:16," "vertical clips," or pastes a video file path and wants social-ready cuts.

Clipify

Find compelling moments in a video, cut them as standalone clips, optionally reformat 16:9 → 9:16 (face-pan or split-screen), time-warp pans for tighter reveals, and burn opus-style word-by-word captions.

Modes

Pick one before Step 1. The choice changes how you find moments and whether captions apply.

• **Dialogue mode** (default for podcasts, interviews, talking-heads): Whisper transcribes, you scan for punchlines / reactions / awkward pauses. → Step 1A. Captions in Step 5.
• **Action mode** (woodchopping, sports, percussion, drumming, anything with repeated impact sounds): skip Whisper entirely, run `detect_transients.py` to find each strike, cut on each one. → Step 1B. Skip Step 5; the strike sounds *are* the rhythm.
• **Hybrid**: dialogue mode for talking sections, action mode for the rest, intercut. Use both 1A and 1B.

If the user says "I'm not talking in this," you're in action mode. If they say "cut on each X" (chop, hit, punch, drumbeat, swing, footstep), you're in action mode. Default to dialogue mode otherwise.

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:** add `-hwaccel videotoolbox` for decode and `-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
• `detect_transients.py` — find sharp impact sounds (action mode); see `--help`

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

---

Workflow

Step 1A — Find dialogue moments (dialogue mode)

mkdir -p /tmp/clipify
ffmpeg -y -hwaccel videotoolbox -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:

• **Punchlines and reactions:** words like "what", "wait", "no way", laughter, "haha", swearing
• **Reversal moments:** setup question → unexpected answer
• **Awkward pauses:** Whisper segment with long gap, or filler ("uh", "um")
• **Self-roast / quotable one-liners:** short declarative sentences that stand alone
• **Audio peaks:** detect via `ffmpeg -af volumedetect` or look for rapid back-and-forth (alternating short Whisper segments)

For each candidate, propose: `[start, end, why-it's-funny, suggested title]`. Aim for 10–25s clips. Show the list and let the user confirm/pick.

Step 1B — Find each strike (action mode)

Replace Whisper with audio-transient detection. The detector finds sharp impacts (axe-on-wood, fist-on-pad, drumhead, ball-on-bat, wood landing on a pile) by computing spectral flux on the 1–6 kHz band — the broadband impulse an impact creates that ambient/wind doesn't.

ffmpeg -y -hwaccel videotoolbox -i "$VIDEO" -vn -ac 1 -ar 16000 /tmp/clipify/audio.wav
python3 <skill-dir>/scripts/detect_transients.py /tmp/clipify/audio.wav \
  --min-flux 30 --min-gap 0.6 > /tmp/clipify/strikes.json

Tuning:

• `--min-flux 30` is the sane default. Real impacts register 50–300; ambient/wind sits at 5–15. Too many false positives → raise to 40–50. Too few hits → drop to 20.
• `--min-gap 0.6` (seconds) is the closest two strikes can be. Fast drumming may need 0.2; chopping is fine at 0.6–1.0.
• `--band 1000:6000` covers wood/metal/glass impacts. Heavy thuds (kick drum, body shots) live lower (`--band 200:2000`); whistles/clinks/snare cracks higher (`--band 4000:10000`).
• For multi-clip sources (a folder of phone clips), run the detector on each clip and pick a varied set across angles. Aim for 50–65 strikes for a 45–60s montage at ~0.9s per shot.

Each "shot" should start ~0.4s before the strike (showing wind-up) and end ~0.5s after (strike + brief follow-through). That's ~0.9s per beat.

Step 1.5 — Verify each candidate is in frame

**Always do this before rendering**, in either mode. Audio detection finds the *sound* of an event; the camera might not have captured it. Skipping this is the #1 way to get user feedback like "you cut on chops where I'm not even visible."

# Sample one frame at each candidate strike time T:
for T in $STRIKE_TIMES; do
  ffmpeg -y -ss "$T" -i "$VIDEO" -frames:v 1 -vf scale=320:-1 \
    /tmp/clipify/verify_${T}.jpg
done

Read each verify image and drop the candidate if:

• The subject is bent down, off-frame, or behind an obstacle
• A thumb is on the lens (yes, this happens — and you only catch it visually)
• It's clearly a different sound source (door slam, dropped tool) that triggered the detector

For ~