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find-golden-segments
Find naturally clean, coherent video segments worth keeping (selection over repair)
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Find naturally clean, coherent video segments worth keeping (selection over repair)
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Generate karaoke-style word-level timestamps by aligning script text to audio using Qwen3-ForcedAligner + jieba for Chinese word segmentation. Use when the user says 'align captions', 'karaoke timestamps', 'word timestamps', 'caption alignment', 'sync text to audio'.
Analyze raw video content using Gemini to identify speakers, topics, key moments, and potential clip opportunities
Audio processing utilities - noise reduction, normalization, enhancement
Extract a video segment using FFmpeg with precise start/end times
Text-guided audio source separation using SAM-Audio via mlx-audio
ASR with ~30ms timestamp precision using Qwen3-ASR + ForcedAligner
| name | find-golden-segments |
| description | Find naturally clean, coherent video segments worth keeping (selection over repair) |
Use this skill to identify the best moments in raw footage - segments that are already clean and don't need repair.
Selection over Repair: Instead of trying to fix broken footage by cutting out fillers, find the moments that are naturally good.
python skills/find-golden-segments/find_golden.py <video_path>
# Examples
python skills/find-golden-segments/find_golden.py video.MOV
python skills/find-golden-segments/find_golden.py video.MOV --min-duration 15
{
"golden_segments": [
{
"start": "02:15",
"end": "02:38",
"duration_sec": 23,
"score": 9,
"speaker": "Speaker Name",
"topic": "Brief topic",
"quote_preview": "First few words...",
"quality_notes": "Why this segment is good"
}
],
"summary": {
"total_duration": 140,
"golden_duration": 45,
"segments_found": 3
}
}
Only segments scoring 7/10 or higher are included. Lower-quality sections are skipped entirely.