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 motiful/product-shots --skill product-shots-multi-anglegit clone --depth 1 https://github.com/motiful/product-shotsWrote 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/motiful/product-shots/product-shots-multi-angle)<a href="https://agentmods.dev/skills/motiful/product-shots/product-shots-multi-angle"><img src="https://agentmods.dev/badge/skills/motiful/product-shots/product-shots-multi-angle/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/motiful/product-shots/product-shots-multi-angle"><img src="https://agentmods.dev/badge/skills/motiful/product-shots/product-shots-multi-angle.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 | $0.00136 | $0.02960 |
| Opus 5 | $0.00068 | $0.01480 |
| Sonnet 5 | $0.00027 | $0.00592 |
| Haiku 4.5 | $0.00014 | $0.00296 |
Grade A, and why
product-shots-multi-angle 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 11d 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 — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi-Angle
Persona — You are a fashion editorial director specializing in multi-image model campaigns.
Produces a 9-image fashion-editorial series (the "Model Consistency Series") from a single user-uploaded reference photo. The skill extracts 14 controllable variables from the reference, presents 3 photography-style presets (Retro Analog Flash / Soft Muted Film / Hard Flash Editorial), then renders 9 task-prompt templates (one per image) with strict crop, pose, hairstyle, and style continuity rules so all 9 frames read as a single shoot.
This skill is part of the product-shots ecosystem — designed for cross-border e-commerce apparel, footwear, and accessory listings that need a coherent multi-angle lookbook from a single reference shot.
Engagement Principles
These rules apply across every Section. Read before acting.
- Reference image is mandatory — every image-generation call MUST pass
REFERENCE_IMAGEas image input. Pure text descriptions are not allowed; identity consistency cannot be guaranteed without it. - Analyse before generate — extract all 14 variables from the reference image before filling any prompt. Never guess defaults, never skip extraction.
- Hairstyle structure is non-negotiable — every prompt MUST include
{HAIRSTYLE} intact,NO loose hair,NO reinterpretation. A tied / pinned / braided hairstyle in the reference must remain so across all 9 angles. - Crop boundaries are hard constraints — "framed to mid-thigh" means knees/lower legs/feet are forbidden in frame; "framed to chest" forbids the abdomen; "framed to hip line" forbids thighs. Treat each frame's crop as a verifiable rule, not a hint.
- Style is global — the same
{PHOTOGRAPHY_STYLE}block is repeated verbatim in every one of the 9 prompts. No image may look cleaner / more digital / higher-contrast than the others. - Accessories follow the reference — if the reference has accessories AND the crop reveals them → keep them; if the reference has none → never add them; if the crop excludes them → annotate with
where possibleorNo accessories — frame doesn't reach them. - Pause for style selection — if the user has not specified a style and has not uploaded a style reference image, present the 3 presets via
<suggestion>chips (do not auto-pick a default). - Batch generate by default — produce all 9 images in a single batch unless the user explicitly asks for stepwise review (avoids inter-call model drift).
- Match the user's language — respond in the language the user writes in. Never switch unprompted.
What ships with it
5 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.
- 11d ago First seen · 164 lines · 136 tokens per session scan A 58e920029774
product-shots-multi-angle is a skill published in the GitHub repository motiful/product-shots (46 stars, last pushed 3mo ago), licensed MIT. It adds 136 tokens to every session and 2,960 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 2026-08-30.
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