oxbshw/watch-skill

extracting-structure

The user wants structure pulled out of a watched video — "make chapters for this video", "where does the bug appear in this recording", "turn this screen recording into a bug report", "how strong is my intro/hook".

소스 보기

사용 방법

  1. 설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
  2. 필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
  3. 입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.
npx skills add https://github.com/oxbshw/watch-skill --skill extracting-structure

시작 전 확인

  • 설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
  • 별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.
원본 Skill 문서

원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.

Extracting structure

Three extractors turn an already-watched video into something structured. All of them answer straight from the persistent index — deterministic, no extra model calls. The video must be watched first (see watching-videos).

Chapters

bash
watch-skill extract chapters <video_id-or-source>

Titled chapters with start/end timestamps, built from scene changes plus transcript topic shifts. Good for long tutorials, lectures, meetings — give the user the chapter list with timestamps, not a prose summary.

Bug report from a screen recording

bash
watch-skill extract bug-report <video_id-or-source>

Pinpoints where an error appears: timestamp, the exact frame, the on-screen error text as OCR read it, and the steps that led up to it (from the transcript/actions preceding the failure). Returns found: false honestly when no error signal exists — report that as "no error found in the recording", not as an error.

Paste the output into the user's issue tracker format when they ask for a fileable report; the frame path is the attachment.

Hook analysis (creators)

bash
watch-skill extract hook <video_id-or-source> [--seconds 15]

Scores the opening seconds on four measured axes — attention trigger in the opening line, speech pacing, visual change rate, on-screen text — each with an actionable critique, plus a 0–100 score and verdict. Give the user the per-axis critiques; the number alone helps nobody.

관련 작업

같은 저장소의 Skills

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모든 Skills
oxbshw
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asking-with-evidence

The user asks a question about a video that was already watched or indexed — "what did they say about X", "what error code appears", "what happens at 2:30", "does the video show Y". Use this to answer from the persistent index with timestamped evidence and a confidence score instead of re-watching or guessing.

설치 수
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configuring-vision

The user wants to connect an LLM or vision provider, already has an API key, asks "can I use OpenAI/Anthropic/Gemini/OpenRouter", wants local Ollama, or needs different cheap and strong models. Use this to configure provider-neutral visual understanding without tying Watch Skill to one agent or model vendor.

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learning-from-mistakes

The user corrected an answer about a video — "no, it actually says X", "that's the wrong timestamp", "you misread the error code" — or asks why a video answer was wrong. Use this to record the correction as a lesson so future answers on similar questions improve, and to show what the system has learned and saved.

설치 수
2
GitHub Stars
503
업데이트
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recovering-from-errors

A Watch Skill command failed, video download broke, ffmpeg or yt-dlp is missing, a provider returned an error, local vision stopped, or the user asks "why can't it watch this" or "repair my setup". Use this to diagnose and apply the engine's structured fix before retrying the original operation.

설치 수
2
GitHub Stars
503
업데이트
10월 14일