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 carousel-buildergit 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/carousel-builder)<a href="https://agentmods.dev/skills/naveedharri/benai-skills/carousel-builder"><img src="https://agentmods.dev/badge/skills/naveedharri/benai-skills/carousel-builder/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/carousel-builder"><img src="https://agentmods.dev/badge/skills/naveedharri/benai-skills/carousel-builder.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 | $0.00132 | $0.01749 |
| Opus 5 | $0.00066 | $0.00874 |
| Sonnet 5 | $0.00026 | $0.00350 |
| Haiku 4.5 | $0.00013 | $0.00175 |
Grade A, and why
carousel-builder 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 4d 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Carousel Builder
Turn one piece of source content that is handed to you into a finished Ben AI image-first authority carousel (Type B) and export a print-ready PDF. Slides render with Higgsfield GPT Image 2; the footer bar is composited by code so the logo, avatar, and page numbers are always correct.
Scope. This skill only builds the carousel from content it is given, and returns the PDF. It does not scan inboxes, choose which email to use, schedule anything, or post to Slack. An orchestrator (a person, or the daily routine) finds the source, invokes this skill, and handles delivery. Keep this skill focused on the build.
The carousel must make its point on its own (assume the social caption is one line).
Before you start (every run)
- Read
references/brand-and-copy.md(palette, type, voice, copy architecture, hard rules). - Read
references/render-pipeline.md(exact render + footer + PDF commands, both the Higgsfield connector and the CLI paths). - Scan
assets/templates/type-b-image-first.png(the look) andassets/example/(a finished reference carousel: 6 slides).
Step 1: Read the source, find the ONE point
Accept whatever content is provided: a newsletter body, a YouTube transcript, LinkedIn/post copy, or a brief. Identify the single argument the carousel makes: one point, backed up. If the source only teases (e.g. "watch my video"), reconstruct the real substance so the carousel stands alone. If the source has several disconnected ideas, pick the most useful one.
Step 2: Slide count and architecture (3-10)
Pick the count from the content (typical 5-8, lower lands harder). Build an arc:
- Slides 1-3 = big-header, high-impact. Cover/hook (with Ben's portrait), problem, first turn to the solution. One large headline + one yellow highlight + short body.
- Middle slides = text-heavier substance. Deliver the actual method: a smaller header plus 2-4 short bullets, numbered steps, or a simple labeled graphic. One idea per slide. This is what makes the carousel self-contained.
- Final slide = a CTA that makes sense (follow / watch the full breakdown / grab the skill). Never a CTA that leans on context the slides never gave.
What ships with it
13 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.
- assets/example/slide-01.png 2572 KB
- assets/example/slide-02.png 1790 KB
- assets/example/slide-03.png 1739 KB
- assets/example/slide-04.png 2009 KB
- assets/example/slide-05.png 1839 KB
- assets/example/slide-06.png 1715 KB
- assets/fonts/mono.ttf 97 KB
- assets/logo/benai-smiley.png 39 KB
- assets/portrait/ben-portrait.png 1220 KB
- assets/templates/type-b-image-first.png 206 KB
- references/brand-and-copy.md 3.6 KB
- references/render-pipeline.md 4.6 KB
- scripts/footer.py 4.7 KB runs code
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.
- 4d ago First seen · 100 lines · 132 tokens per session scan A ead91eed3a5a
carousel-builder is a skill published in the GitHub repository naveedharri/benai-skills (61 stars, last pushed 6d ago), licensed MIT. It adds 132 tokens to every session and 1,749 once invoked, about $0.0007 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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