使用方法
- 复制安装命令，在自己的智能体环境安装前先检查源码包。
- 从原始文档核对所需工具、凭据和支持的宿主。
- 说明输入材料与期望交付物，并对照来源检查输出。
npx skills add https://github.com/oxbshw/watch-skill --skill asking-with-evidence开始前需了解
- 安装不包含模型订阅、API 额度或访问文件的权限。
- 仓库星数和安装量反映活跃情况，不等于独立质量或安全测试。
按源仓库内容呈现，保留标题、案例、代码、表格、链接以及原文引用的演示图片。
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.
