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name: asking-with-evidence version: "1.4.3" description: 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. license: MIT user-invocable: true allowed-tools: Bash, Read

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.