Gameplay clip extractor
Skill rahulcommercial/claude-code-for-content-creators/skills/gameplay-clip-extractor
Claude Code skills for solo content creators — gameplay clips, Reels pipeline, IG captions, thumbnail A/B testing.
npx -y skills add rahulcommercial/claude-code-for-content-creators --skill gameplay-clip-extractorAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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- 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Finds highlight moments in long gameplay recordings (BGMI, FIFA, Valorant, COD) using audio peak detection, optional scoreboard OCR, and pacing heuristics. Outputs timestamps + suggested clip ranges. Trigger when user has raw gameplay footage and wants to extract highlights, kill clips, goals, or shareable moments.
SKILL.md
2.4 KB, 611 tokens by cl100k_base, as published. Nobody here has run it
Gameplay Clip Extractor
Reduce a 1-hour gameplay capture to a list of 10-second clips worth posting.
When to use
- User has raw
.mp4/.movgameplay footage (their own, no piracy). - User wants timestamps for highlights without scrubbing manually.
- User wants the actual clipped files written to disk.
Detection signals (combine, don't pick one)
1. Audio peak detection (cheapest, works everywhere)
Gunfire, goal whistles, killstreak voice lines, crowd reactions — all show up as audio amplitude spikes.
ffmpeg -i raw.mp4 -af "silencedetect=noise=-30dB:d=0.5" -f null - 2>&1 \
| grep silence_end
Use the inverse — long non-silent stretches with sudden RMS jumps are candidates.
Python (cleaner):
import librosa, numpy as np
y, sr = librosa.load("raw.mp4", sr=22050, mono=True)
rms = librosa.feature.rms(y=y, frame_length=2048, hop_length=512)[0]
peaks = np.where(rms > rms.mean() + 2 * rms.std())[0]
peak_times = librosa.frames_to_time(peaks, sr=sr, hop_length=512)
2. Scoreboard / kill-feed OCR (game-specific, higher accuracy)
- BGMI / COD: top-right kill feed → run Tesseract on that ROI every 1s.
- FIFA: scoreboard score-change → diff frames at fixed coords.
- Cache last value; emit event only on change.
3. Pacing heuristic
Cluster nearby peaks (within 3s) into single moments. A real highlight is usually 1 spike, not a noise burst.
Output
[
{"start": 142.3, "end": 152.8, "label": "kill burst (3 audio peaks)", "score": 0.82},
{"start": 384.1, "end": 394.6, "label": "score change 1→2", "score": 0.95}
]
Then clip with FFmpeg:
ffmpeg -ss 142.3 -i raw.mp4 -t 10.5 -c copy clips/clip_001.mp4
Heuristics for "is this postable?"
- ≥ 2 audio peaks in a 10s window
- Clip ends on a resolved moment (kill confirmed, goal scored), not mid-action
- No long static frames (loading, respawn screen)
What NOT to do
- Don't auto-publish. Always present clips for human review.
- Don't OCR the entire frame — that's slow and noisy. Define ROIs per game.
- Don't process clips you don't own the rights to.
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.