How to use
- Copy the installation command and review the source package before installing it in your own agent environment.
- Check the source document for required tools, credentials and supported hosts.
- Describe your input and desired deliverable; review the output against the source material.
npx skills add https://github.com/oxbshw/watch-skill --skill asking-with-evidenceBefore you start
- Installation does not include a model subscription, API allowance or permission to access your files.
- Repository stars and installation counts describe activity, not an independent quality or security test.
Rendered from the source repository. Headings, examples, code, tables, links, and referenced images are preserved.
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.
