Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add oxbshw/watch-skill --skill asking-with-evidencegit clone --depth 1 https://github.com/oxbshw/watch-skillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/oxbshw/watch-skill/asking-with-evidence)<a href="https://agentmods.dev/skills/oxbshw/watch-skill/asking-with-evidence"><img src="https://agentmods.dev/badge/skills/oxbshw/watch-skill/asking-with-evidence/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/oxbshw/watch-skill/asking-with-evidence"><img src="https://agentmods.dev/badge/skills/oxbshw/watch-skill/asking-with-evidence.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
- Socket pass
- Snyk pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00074 | $0.00528 |
| Opus 5.5 | $0.00030 | $0.00211 |
| Sonnet 5.5 | $0.00015 | $0.00106 |
| Haiku 4.5 | $0.00007 | $0.00053 |
Grade A, and why
asking-with-evidence scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago Changed a214879ee6e9
- 1mo ago Changed 0b5061b6c6d7
- 1mo ago Changed aa3e3ec9685d
- 1mo ago First seen · 63 lines · 74 tokens per session scan A 5f39326c6b9a
asking-with-evidence is a skill published in the GitHub repository oxbshw/watch-skill (532 stars, last pushed yesterday), licensed MIT. It adds 74 tokens to every session and 528 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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