Asking with evidence
SkillMediaThe 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.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Asking with evidence skill
What this skill tells your AI
The instructions your AI receives, as published by oxbshw/watch-skill in skills/asking-with-evidence/SKILL.md and read by ahel’s review.
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.
Signals
- GitHub stars
- 370
- Forks
- 52
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
asking-with-evidence- Source
- github.com/oxbshw/watch-skill