oxbshw/watch-skill

sharing-results

The user wants to share or hand off what was found in a video — "send this analysis to my team", "make a page I can share", "export the findings", "give me something I can attach to the ticket".

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 sharing-results

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.

Sharing results

An analysis that lives only in this chat dies with it. The viewer turns an analyzed video into a single HTML file anyone can open — no server, no internet, no Watch Skill install on the other end.

Generate the page

bash
watch-skill viewer <video_id-or-source> [-o <path>]

The file contains, self-contained (frames inlined as data URIs, zero external requests):

  • a timeline with key-frame cards and timestamps,
  • the transcript and all on-screen text (OCR),
  • every question asked about the video so far, with its answer,

confidence badge, and the exact timestamped evidence cited.

Answer questions FIRST, then generate. The page includes cached answers, so ask what the user cares about before rendering — a viewer generated before any questions holds only frames and transcript.

Hand-off patterns

  • Bug ticket: run watch-skill extract bug-report first, then the

viewer — the ticket gets the structured report, the viewer link gives reviewers the evidence.

  • Review request: send the file itself (it is one .html, typically well

under a few hundred KB); it opens in any browser, RTL text renders correctly, nothing phones home.

관련 작업

같은 저장소의 Skills

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oxbshw
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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.

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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.

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