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 SupercmoHQ/superCMO-skills --skill generating-storyboardsgit clone --depth 1 https://github.com/SupercmoHQ/superCMO-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/supercmohq/supercmo-skills/generating-storyboards)<a href="https://agentmods.dev/skills/supercmohq/supercmo-skills/generating-storyboards"><img src="https://agentmods.dev/badge/skills/supercmohq/supercmo-skills/generating-storyboards/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/supercmohq/supercmo-skills/generating-storyboards"><img src="https://agentmods.dev/badge/skills/supercmohq/supercmo-skills/generating-storyboards.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 | $0.00106 | $0.03205 |
| Opus 5.5 | $0.00042 | $0.01282 |
| Sonnet 5.5 | $0.00021 | $0.00641 |
| Haiku 4.5 | $0.00011 | $0.00320 |
Grade A, and why
generating-storyboards 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.
How it starts
The opening of the file, as written. The whole thing — 223 lines — stays where its author put it; the contents beside it link to each section on GitHub.
The storyboard
One sheet per clip, vertical panels in a row, each panel one action, left to right. Panels are numbered within a sheet; sheets are numbered across the video.
The sheet is always 16:9 and its panels are always vertical, whatever shape the finished video
is. Neither follows the delivery ratio, and neither is the plan's to set.
The plan settles how many sheets there are, what happens in each clip and how long it runs, which images come in as references, and the look. How a clip divides into panels is settled here.
Anything the plan specifies wins. The defaults below fill gaps only.
Workflow
One sheet at a time. Run all six steps for the first sheet, then run all six again for the second, and so on. Sheets cannot be built in parallel or in one call — each prompt needs the previous sheet's finished image in hand.
Step 1: Collect what's needed
- An image of the person — wherever a person appears in any panel.
- Product image or images — where the video has a product.
- The previous sheet — from the second sheet on.
- What happens in this clip, and how many seconds it runs.
- How many sheets there are, and which one this is.
- The look, where the plan names one.
What a supplied image is for comes from the brief, not from its contents — a person in a frame doesn't make it the person reference, and one image can't serve as both the person and the product.
Read every supplied image with image_analysis before deciding anything: what the place looks
like, what they're wearing, and for a product, its colours, its shape, its finish, and how big it is.
Skip this where the image is already known.
Where any of this is missing, ask for it rather than assuming it.
Step 2: Settle the sheet
- References — every one goes in twice: attached as
reference_imageson the generate call, and labelled Image 1, Image 2, Image 3 in the prompt text. Same order both times — the product image or images, the person's image, then the previous sheet once there is one. Say once what each is, then refer to it by label. A label with nothing attached points at nothing, and unlabelled attachments let the model pick its own subject out of the set. - The person — pointed at by label, never described. State that wherever they appear it is the same person, with face, hair, build and skin tone identical throughout. Don't describe their age, ethnicity, build, hair, makeup or features — the reference supplies all of it, and words describing it fight the image. They need not be in every panel: a tight product panel may show only their hands, or nobody at all.
- Setting — where they are, when, and how it's lit. Unless the plan sends them elsewhere, carry the location, the hour and the direction of the light straight from the person's image.
- Light — from a named direction. The plan's look sets its quality — soft and neutral, dramatic and low-key, or clean studio light. No golden hour, sunset, or amber cast unless the plan asks for it.
- Wardrobe — the garments and their colours, named. From the person's image by default.
- Look — the look the plan names, carried into every sheet. Where the plan names none, a clean, naturally-lit photographic still.
- Product name — one short noun phrase, used word for word in every panel that mentions it.
- Product size — a measurement, so it can be held against the hand at its real size.
What ships with it
1 file 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.
- 1mo ago First seen · 223 lines · 106 tokens per session scan A 92fa1e06de38
generating-storyboards is a skill published in the GitHub repository SupercmoHQ/superCMO-skills (242 stars, last pushed 6d ago), licensed Apache-2.0. It adds 106 tokens to every session and 3,205 once invoked, about $0.0004 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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