Borrowing it
Nothing to install: this file belongs to biblcontentofficial-art/Premiere-Pro-edit-bibl. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/biblcontentofficial-art/Premiere-Pro-edit-bibl/main/.claude/skills/cut-editing/SKILL.mdgit clone --depth 1 https://github.com/biblcontentofficial-art/Premiere-Pro-edit-biblWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/biblcontentofficial-art/premiere-pro-edit-bibl/cut-editing)<a href="https://agentmods.dev/skills/biblcontentofficial-art/premiere-pro-edit-bibl/cut-editing"><img src="https://agentmods.dev/badge/skills/biblcontentofficial-art/premiere-pro-edit-bibl/cut-editing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/biblcontentofficial-art/premiere-pro-edit-bibl/cut-editing"><img src="https://agentmods.dev/badge/skills/biblcontentofficial-art/premiere-pro-edit-bibl/cut-editing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00143 | $0.01436 |
| Opus 5.5 | $0.00057 | $0.00574 |
| Sonnet 5.5 | $0.00029 | $0.00287 |
| Haiku 4.5 | $0.00014 | $0.00144 |
Grade A, and why
cut-editing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
컷편집 실행
engine/auto_cut.py 통합 엔진을 운용해 러프컷을 만든다. 엔진이 무거운 작업(무음감지·전사·말더듬/중복·NG 제거·숨소리 축소·음성보정·자막)을 결정적으로 처리하므로, 올바른 설정으로 실행하고 결과를 검증하는 게 핵심.
전문 편집자 기준 기본 동작: 무음 제거(끝음 보존) · 추임새 보존 · 말더듬/중복 제거 · NG(재촬영) 컷 제거(재촬영 신호어+재시도가 확인될 때만, 오탐 방지) · 숨소리 축소 · 음성보정(de-esser + 노이즈플로어 자동측정 후 조건부 노이즈제거 + 압축 + -14 LUFS).
0단계: 프리셋 자동 추천 (먼저)
어떤 프리셋을 쓸지 모르면 먼저 영상을 분석한다:
python3 engine/analyze_video.py "원본영상.mp4"
길이·라우드니스·무음 비율을 측정해 보수/표준/공격을 추천하고 config 값까지 제안한다. 추천을 그대로 따르거나, 기획자(20_plan.md)가 정한 프리셋이 있으면 그것을 우선한다.
실행
# 프로젝트 루트에서 실행 (프리셋은 위 추천 또는 기획자 지정)
python3 engine/auto_cut.py "원본영상.mp4" --preset 표준
- 73분 영상 기준 첫 실행 ~5분(전사 포함). 전사 캐시(
_words.json) 있으면 ~2분. - 무거우니 백그라운드로 실행하고 완료를 기다린다.
프리셋 선택 기준 (2026-06 정책: 추임새는 기본 보존, 숨소리만 축소)
| 프리셋 | 언제 | 효과 |
|---|---|---|
| 보수 | 자연스러움 최우선, 잔잔한 인터뷰 | 무음도 최소한만. 추임새 살림 |
| 표준 | 대부분의 토크/라이브 (기본) | 무음+숨소리+더듬 정리, 추임새(아·어·음·뭐) 살림 ( |
| 공격 | 최대한 타이트, 군더더기 많은 영상 | 추임새·망설임까지 전부 제거(그래서·이제·근데까지) |
세부 조정은 프로젝트 루트 config.json(config.json.example 참고)으로. 모델·임계값·필러목록 override.
산출물 (output/)
<base>_cut.xml— 프리미어 '불러오기' 하면 편집 가능한 시퀀스<base>_cut_audio.wav— 정리된 -14 LUFS 오디오 (XML이 자동 연결)<base>_cut.srt— 컷 정렬 자막 초안<base>_cut_report.txt— 잘린 내용 전체(타임코드별)<base>_words.json— 단어 단위 전사(리서처·자막이 재사용)
제1원칙: 자연스러움 > 최대 제거
추임새(아·어·음·뭐)는 말의 호흡이라 기본 보존한다(비블 확정 — 다 자르면 AI가 읽는 것처럼 됨). 대신 숨소리(노이즈성 들숨/날숨)만 무음처럼 줄인다. 잘라낸 뒤 전체 맥락에서 부자연스러우면 그 컷은 실패. 제거율·개수만 보지 말고, 이어 봤을 때 뚝뚝 끊기지/숨 가쁘지/리듬 깨지지 않는지 본다.
검증 (실행 후 필수)
- 자연스러움 (최우선) —
_cut_report.txt의 '자연스러움 주의(컷 촘촘)' 목록 확인. 컷이 몰린 구간은 부자연 위험 → 디렉터에 보고하거나 한 단계 보수 프리셋 재실행. - 제거율 — "총 제거 %" 확인. 표준 3~10% 정상(추임새 살리므로 낮음), 12% 초과면 과제거 의심. 공격은 13~19% 정상.
- 과제거 점검 —
_cut_report.txt에서 의미 있는 단어(접속사·핵심어)가 잘렸는지. 잘렸으면 보수적 프리셋 재실행. - 프레임 무결성 — XML의 갭/겹침이 0인지(엔진이 '검증' 항목에 자동 표시).
왜 이렇게 하는가
- 엔진 실행은 결정적이라 매번 같은 결과 → 신뢰 가능. 하지만 영상마다 발화 습관이 달라 제거율이 튄다 → 결과 검증은 매번 한다.
- 과제거는 되돌리기 번거로우므로(원본 가장자리 드래그 필요), 의심되면 처음부터 보수적으로.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 61 lines · 143 tokens per session scan A 0da8cf5e2c13
cut-editing is a skill published in the GitHub repository biblcontentofficial-art/Premiere-Pro-edit-bibl (24 stars, last pushed 3mo ago), licensed MIT. It adds 143 tokens to every session and 1,436 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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