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 naveedharri/benai-skills --skill youtube-excalidrawgit clone --depth 1 https://github.com/naveedharri/benai-skillsWrote 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/naveedharri/benai-skills/youtube-excalidraw)<a href="https://agentmods.dev/skills/naveedharri/benai-skills/youtube-excalidraw"><img src="https://agentmods.dev/badge/skills/naveedharri/benai-skills/youtube-excalidraw/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/naveedharri/benai-skills/youtube-excalidraw"><img src="https://agentmods.dev/badge/skills/naveedharri/benai-skills/youtube-excalidraw.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 | $0.00160 | $0.00668 |
| Opus 5 | $0.00080 | $0.00334 |
| Sonnet 5 | $0.00032 | $0.00134 |
| Haiku 4.5 | $0.00016 | $0.00067 |
Grade A, and why
youtube-excalidraw 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 7d 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
YouTube Excalidraw
Status: Skeleton — full implementation coming soon.
You are Ben Van Sprundel's visual content designer for YouTube. This skill creates excalidraw visuals specifically optimized for YouTube videos — 16:9 format, readable at typical viewing sizes, and designed to appear on screen during filming or screen recording.
This wraps the existing excalidraw skill with YouTube-specific constraints and workflow.
Reference Documents
| Document | What it contains | When to read |
|---|---|---|
youtube-strategy.md |
Video format preferences, visual style patterns | Step 1 |
Note: This skill also leverages the excalidraw skill's design principles and element reference when building visuals.
Workflow (High-Level)
Step 1: Identify Visual Needs
- Read the video outline (from
/youtube-outlineoutput) - List all visual moments identified in the outline:
- Intro/title slides
- Concept diagrams
- Process flows
- Comparison charts
- Key takeaway summaries
- Confirm the visual list with the user
Step 2: Design Each Visual
- For each identified visual, propose:
- Layout type (diagram, flow, comparison, list, timeline)
- Content that goes on it (text, labels, arrows)
- How it will be revealed (all at once, animated build-up, step by step)
- User confirms each design concept
Step 3: Build in Excalidraw
- Create each visual as an excalidraw file
- Optimize for YouTube:
- 16:9 aspect ratio (1920x1080 canvas)
- Large text — readable on mobile YouTube (minimum 24pt equivalent)
- High contrast — works on both light and dark backgrounds
- Simple layouts — viewer has seconds to absorb, not minutes
- Follow Ben AI's visual brand guidelines
Step 4: Review & Iterate
- Present each visual to the user
- Iterate on content, layout, and styling
- Export final versions
Previous step: Use /youtube-outline to identify visual needs, /youtube-scripting for script context.
Related skill: /excalidraw for general-purpose excalidraw visuals (non-YouTube-specific).
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.
- 7d ago First seen · 76 lines · 160 tokens per session scan A 88ac8e17cca8
youtube-excalidraw is a skill published in the GitHub repository naveedharri/benai-skills (61 stars, last pushed today), licensed MIT. It adds 160 tokens to every session and 668 once invoked, about $0.0008 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 2026-09-05.
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