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.
git clone --depth 1 https://github.com/lorena-bordonaba-pau/product-studioWrote 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/commands/lorena-bordonaba-pau/product-studio/product-video)<a href="https://agentmods.dev/commands/lorena-bordonaba-pau/product-studio/product-video"><img src="https://agentmods.dev/badge/commands/lorena-bordonaba-pau/product-studio/product-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/commands/lorena-bordonaba-pau/product-studio/product-video"><img src="https://agentmods.dev/badge/commands/lorena-bordonaba-pau/product-studio/product-video.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00031 | $0.00358 |
| Opus 5 | $0.00015 | $0.00179 |
| Sonnet 5 | $0.00006 | $0.00072 |
| Haiku 4.5 | $0.00003 | $0.00036 |
Grade A, and why
product-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 10d 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
/product-video — Product video
Generates a short product video: a single locked shot with subtle, slow motion. Always reply in the user's language.
Start image provided by the user: $ARGUMENTS
(If empty: look for approved images in output/ from a previous /product-image run and suggest
using the hero; if there are none, ask the user for a product photo.)
Instructions
Follow the flow defined in the product-photography skill (product-studio plugin), section
"Workflow: video", and read references/video.md to pick the motion recipe for the product's
category (footwear → feet in close-up moving slowly; cosmetics → slow rotation; textile →
fabric in a soft breeze; etc.).
Hard rules, no exceptions:
- Camera ALWAYS locked (no zooms, pans or dolly moves).
- Subtle, fine, slow motion. A single ~5 second shot, no cuts, effects or text.
- The product must remain identical to the start image for the whole video.
Before generating, check the config (~/.claude/product-studio/config.json) and the local
learnings. Preflight the cost when the provider supports it — video is far more expensive than
images. After generating, review several frames of the video: if the product warps or changes
color, regenerate with a specific correction (max 2 attempts) and record the learning.
Deliver to output/<product>/video-01.mp4.
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.
- 10d ago First seen · 32 lines · 31 tokens per session scan A 137befe89ffa
product-video is a command published in the GitHub repository lorena-bordonaba-pau/product-studio (6 stars, last pushed 2mo ago), licensed MIT. It adds 31 tokens to every session and 358 once invoked, about $0.0002 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 2026-08-31.
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