oxbshw/watch-skill

video-memory

The user asks about videos watched in the past or across sessions — "have we watched anything about X", "which video showed that error", "what did that meeting decide", "search my videos", or a question that spans several videos.

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 video-memory

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.

Video memory

Every video ever watched on this machine is still here — frames, OCR, transcripts, and past answers, in one persistent index under ~/.watch-skill/. You never have to say "I don't remember that video." Sessions don't expire it; watching more videos makes it more useful.

What's in the library?

bash
watch-skill list

Every indexed video: id, title, duration, when it was analyzed. Check here before watching anything — it may already be in memory from a previous session.

Find something across all of it

bash
watch-skill search "<phrase or keywords>"

Hybrid keyword + semantic search across every video, any language (Arabic folding, CJK and Thai segmentation are handled). Hits come back with video + timestamp; follow up on a hit with:

bash
watch-skill ask <video_id> "<the user's actual question>"
watch-skill ask <video_id> "what is on screen at <timestamp>?"

Questions that span videos

"What did we decide about the pricing page across those meetings?" — questions no single video answers go to the library synthesizer:

bash
watch-skill library ask "<the question>"

It answers from distilled notes across every indexed video, drills into real evidence, and cites video + timestamp for each finding. When it says the library does not clearly answer, trust that — do not stitch a guess together from weak search hits. watch-skill library overview shows what the library knows (videos, note counts, entities recurring across videos).

For "which video showed X" (locate, not synthesize), plain search is the right tool; then ask the hit video.

Batches build memory fast

A playlist or a folder of recordings goes in as one call — watch-skill batch "<playlist-or-folder>" — and the whole set becomes searchable memory (see watching-videos).

관련 작업

같은 저장소의 Skills

더 많은 Skills

모든 Skills
oxbshw
커뮤니티

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
2
GitHub Stars
503
업데이트
10월 14일
oxbshw
커뮤니티

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.

설치 수
2
GitHub Stars
503
업데이트
10월 14일
oxbshw
커뮤니티

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". Use this for deterministic extraction from the index — chapters with timestamps, a fileable bug report with the exact frame, or a scored hook analysis.

설치 수
2
GitHub Stars
503
업데이트
10월 14일
oxbshw
커뮤니티

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
10월 14일