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 zkbys/whiteboard --skill whiteboard-videogit clone --depth 1 https://github.com/zkbys/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/zkbys/whiteboard/whiteboard-video)<a href="https://agentmods.dev/skills/zkbys/whiteboard/whiteboard-video"><img src="https://agentmods.dev/badge/skills/zkbys/whiteboard/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/zkbys/whiteboard/whiteboard-video"><img src="https://agentmods.dev/badge/skills/zkbys/whiteboard/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 | $0.00096 | $0.01652 |
| Opus 5 | $0.00048 | $0.00826 |
| Sonnet 5 | $0.00019 | $0.00330 |
| Haiku 4.5 | $0.00010 | $0.00165 |
Grade A, and why
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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Whiteboard Video
Turn the user's topic or rough script into a reviewable whiteboard-video project. Treat this Skill as the only public entrypoint. Use B, C, Creator, D, E, and the orchestrator from the bundled runtime/ as internal implementation modules.
Resolve the installation
- Treat the directory containing this
SKILL.mdasSKILL_ROOT. - Read
SKILL_ROOT/installation.jsonwhen present. - Resolve the bundled runtime as
SKILL_ROOT/runtime/for an installed copy. In a source checkout, resolve the repository root two directories aboveSKILL_ROOT. - Run the deterministic environment check before the first video in a session:
python3 <SKILL_ROOT>/scripts/doctor.py --json --output-dir <OUTPUT_PARENT>
Report the install, render, output, and image statuses separately. Do not treat interactive image handoff as an installation failure.
Start from natural language
Accept a topic, viewpoint, or rough script directly. Do not require the user to create an input file. If the user gives a range such as 30-60 seconds, target about 45 seconds while keeping the final result inside the requested range.
Default the output parent to the user's current working directory, not the managed Skill installation. Create:
<current-working-directory>/whiteboard-runs/YYYYMMDD-HHMMSS-<topic-slug>/
Write the original request to topic_input.txt inside that project directory.
Use a lowercase ASCII topic-slug with letters, digits, and hyphens. Fall back to whiteboard-video when the topic cannot be represented safely. If the path already exists, append -2, -3, and so on; never overwrite an earlier run implicitly.
Execute the internal pipeline
Read these bundled internal instructions before executing their stage. Installed packages name each internal entry INTERNAL_SKILL.md so Agent discovery exposes only whiteboard-video; source checkouts use SKILL.md in the same module directory.
runtime/ip-cognition-script-polisher/INTERNAL_SKILL.mdruntime/ip-hand-drawn-infographic-planner/INTERNAL_SKILL.mdruntime/hand-drawn-infographic-creator/INTERNAL_SKILL.mdruntime/hand-drawn-infographic-video-board/INTERNAL_SKILL.mdruntime/whiteboard-infographic-video-renderer/INTERNAL_SKILL.mdruntime/whiteboard-infographic-pipeline-orchestrator/INTERNAL_SKILL.mdruntime/whiteboard-infographic-pipeline-orchestrator/references/runbook.mdruntime/whiteboard-infographic-pipeline-orchestrator/references/contracts.md
What ships with it
4 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.
- 12d ago First seen · 137 lines · 96 tokens per session scan A ffc82649e783
whiteboard-video is a skill published in the GitHub repository zkbys/whiteboard (59 stars, last pushed 2mo ago), licensed MIT. It adds 96 tokens to every session and 1,652 once invoked, about $0.0005 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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