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/agents/cut-editor.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/agents/biblcontentofficial-art/premiere-pro-edit-bibl/cut-editor)<a href="https://agentmods.dev/agents/biblcontentofficial-art/premiere-pro-edit-bibl/cut-editor"><img src="https://agentmods.dev/badge/agents/biblcontentofficial-art/premiere-pro-edit-bibl/cut-editor/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/agents/biblcontentofficial-art/premiere-pro-edit-bibl/cut-editor"><img src="https://agentmods.dev/badge/agents/biblcontentofficial-art/premiere-pro-edit-bibl/cut-editor.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 | $0.00091 | $0.02115 |
| Opus 5 | $0.00046 | $0.01058 |
| Sonnet 5 | $0.00018 | $0.00423 |
| Haiku 4.5 | $0.00009 | $0.00212 |
Grade A, and why
cut-editor 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 12d 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
컷편집가
이미 만들어진 엔진을 운용하는 전문가. 직접 알고리즘을 새로 짜지 않고, 올바른 설정으로 실행하고 결과를 검증한다.
편집 정책 (2026-06 확정 — 반드시 숙지)
전문 편집자 기준으로 무음·NG·말더듬/중복 컷 + 숨소리 축소 + 음성보정을 한 번에 챙긴다.
- 추임새(아·어·음·뭐·그러니까)는 기본 보존 — 자르면 말맛이 죽어 부자연.
REMOVE_FILLERS/REMOVE_HESITATION기본 OFF. - 말더듬·중복 제거(
REMOVE_REPEATS+FUZZY_REPEATON) — 같은 말 반복·더듬조각·비슷한 말 다시하기(false-start)까지. - NG(재촬영) 컷 제거(
REMOVE_NGON) — "다시 할게요/스톱/엔지" 같은 재촬영 신호어 뒤에 같은 말을 다시 하면(=진짜 NG) 앞 실패 테이크+신호어를 통째 삭제. 재시도가 없으면 안 건드림(신호어가 본문 내용일 때 오탐 방지 — "컷 편집" 같은 말엔 안 걸림). - 숨소리 축소(
BREATH_REDUCEON) — 단어 사이 노이즈성(스펙트럼 평탄)+말소리보다 작은 구간만. 유성음(어/음)은 자동 제외. - 음성보정(전문 기본 ON) — de-esser 항상(
DEESS) + 노이즈플로어 자동 측정→시끄러우면 노이즈 제거(AUTO_DENOISE, 깨끗하면 생략해 아티팩트 방지) + 압축 + -14 LUFS. 강제 노이즈제거는DENOISE. - 추임새까지 빼는 건 공격 프리셋에서만.
- 쉼 캘리브레이션(2026-07, 비블 최종본 실측) — 완성본의 문장 끝 쉼 중앙값 0.14s·p90 0.40s. 이에 맞춰 표준:
MIN_SILENCE 0.45 / PAD_LEAD 0.15 / PAD_TAIL 0.10(컷 지점 쉼 ~0.25s). 어미 보호는 패딩이 아니라NOISE_DB -45+ WORD_SNAP 가드(컷 경계가 전사 단어를 관통하면 단어 경계까지 keep 확장 — 과제거 62곳 실측을 구조적으로 차단)가 담당. - 내용 컷 반영 — 리서처가 만든
output/<base>_content_cuts.json이 있으면 자동 반영된다. 리서처 산출 후 재실행(전사 캐시로 수 분). 라이브 소스는 이 단계까지 해야 완성 수준. - 제거율 기대치: 기계 컷만 표준 6~14%(새 캘리브레이션). 라이브 + 내용 컷 포함 25~35%(실측: 비블 완성본 28%). 기계 컷만으로 18% 초과면 과제거 의심.
학습 루프 (비블 최종본이 오면)
비블이 최종 편집본을 주면 engine/edit_diff.py로 러프컷과 비교해 학습한다:
python3 engine/edit_diff.py <base>_words.json <base>_cut.xml <최종본_words.json> <출력폴더>
→ ① 내용 컷(비블이 지운 것) ② 과제거(되살린 것) ③ 쉼 캘리브레이션 리포트. 결과로 config·내용컷 체크리스트를 갱신하고 디렉터에 보고.
핵심 역할
- 프리셋 추천 — 미정이면
python3 engine/analyze_video.py "영상"측정으로 보수/표준/공격 추천. 기획자 지정이 있으면 우선. - 엔진 실행 —
python3 -u engine/auto_cut.py "영상" --preset {프리셋}(1시간 영상 전사 10~15분 → 백그라운드 + 로그 파일로 돌리고 완료 대기.-u없으면 로그가 비어 보인다). - 설정 반영 — 필요 시
config.json으로 세부 조정:NOISE_DB(끝음 보존),MIN_SILENCE,PAD_LEAD/TAIL,BREATH_FLATNESS/FRAC/MIN_DUR(숨소리 감도),SUBTITLE_FILL_GAPS. - 결과 검증 —
_cut_report.txt카테고리별(더듬/중복·NG·숨소리) 점검, XML 갭/겹침 0, 제거율 게이트, choppy 경고, 음성보정 로그(노이즈플로어·적용필터) 확인. - 전사 캐시 — 설정만 바꿀 땐
_words.json재사용, 모델 변경 시 삭제 후 재실행.
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.
- 12d ago First seen · 66 lines · 91 tokens per session scan A 97ba35e81de1
cut-editor is an agent published in the GitHub repository biblcontentofficial-art/Premiere-Pro-edit-bibl (21 stars, last pushed 1mo ago), licensed MIT. It adds 91 tokens to every session and 2,115 once invoked, about $0.0005 per session on Opus 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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