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 shaomingchan/whiteboard --skill auto-whiteboard-videogit clone --depth 1 https://github.com/shaomingchan/whiteboardWrote 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/shaomingchan/whiteboard/auto-whiteboard-video)<a href="https://agentmods.dev/skills/shaomingchan/whiteboard/auto-whiteboard-video"><img src="https://agentmods.dev/badge/skills/shaomingchan/whiteboard/auto-whiteboard-video/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/shaomingchan/whiteboard/auto-whiteboard-video"><img src="https://agentmods.dev/badge/skills/shaomingchan/whiteboard/auto-whiteboard-video.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector 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 | $0.00069 | $0.00481 |
| Opus 5 | $0.00034 | $0.00241 |
| Sonnet 5 | $0.00014 | $0.00096 |
| Haiku 4.5 | $0.00007 | $0.00048 |
Grade A, and why
auto-whiteboard-video 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 11d 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
Auto Whiteboard Video
Use the repository's canonical pipeline. Do not rebuild stages manually unless troubleshooting a failed stage.
Workflow
- Run
scripts/doctor.pyfrom the repository root. - Save pasted text as a UTF-8
.txtfile under a user-approved input location when no file is provided. - Choose a stable project directory under
output/; reuse it when resuming. - Run the wrapper below and wait for completion.
- Read
composition_report.jsonand report the final MP4 path, resolution, duration, and audio/video delta.
python skills/auto-whiteboard-video/scripts/run_video.py `
--input examples/demo_30s.txt `
--output-dir output/demo `
--project-dir output/demo/latest `
--keep-temp
All arguments are forwarded to auto-whiteboard/scripts/auto_generate.py.
Locked Defaults
- Output:
1920x1080, 30 fps. - Source images: near 16:9; reject clear 3:2, square, and portrait results.
- Visual style:
#F6F1E3background, black marker lines, restrained amber, faceless round-headed people. - Subtitles: size 88, maximum 20 full-width CJK characters per line.
- BGM: configured default track at
-28 dB. - Spoken numbers and display subtitles are normalized separately.
Resume Rules
- Reuse the same
--project-dirto preserve caches. - Use
--force-tts,--force-images,--force-whiteboard, or--force-composeonly when that stage must change. - Do not delete the project directory to fix a single failed scene.
Success Criteria
Require both final_video.mp4 and composition_report.json. Confirm 1920x1080, single-line subtitles, and output_av_delta_seconds < 0.1 before reporting success.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 46 lines · 69 tokens per session scan A a7046013861e
auto-whiteboard-video is a skill published in the GitHub repository shaomingchan/whiteboard (49 stars, last pushed 1mo ago), licensed MIT. It adds 69 tokens to every session and 481 once invoked, about $0.0003 per session on Opus 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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