사용 방법
- 설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
- 필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
- 입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.
npx skills add https://github.com/oxbshw/watch-skill --skill asking-with-evidence시작 전 확인
- 설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
- 별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.
원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.
Asking with evidence
Every watched video sits in a persistent index. Questions about it are answered from that index — text first, frames only when needed — with timestamps, a confidence score, and an honest refusal when the video does not show the answer. Never re-run a watch for a follow-up.
Answer a question
watch-skill ask <video_id-or-original-url> "<question>"Any language works; the answer comes back in the language of the question. The engine escalates on its own when unsure (dense re-sampling, zoom-crop re-OCR, stronger model) and prints a ~N tokens saved line.
Three rules for reading the result:
- Cite the timestamps it gives you; they are real evidence, not
decoration.
- Trust the refusal. When it says the video does not clearly show
the answer, that is the answer. Do not invent one past it.
- Frame paths are listed only when the engine wants you to look
yourself — Read them then (or force with --frames).
"What happens at 2:30?"
Moment questions get a dense window, not a whole-video ask:
watch-skill ask <video_id> "what is on screen around 2:30?"The answer engine pulls frames, transcript and OCR around the moment it resolves. Agents on MCP have a dedicated get_moment tool that takes an explicit timestamp and window; the CLI answers the same question through ask.
Don't know which video? Search them all
watch-skill search "<phrase>"Hybrid keyword + semantic search across every video ever watched, with per-script normalization (Arabic folding, CJK segmentation, Thai segmentation). Follow a hit with ask or moment on that video.
When the user corrects you
Report it so the next answer is better — see the learning-from-mistakes skill.
