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 hermoso-ai/hermoso --skill hermoso-product-photoshootgit clone --depth 1 https://github.com/hermoso-ai/hermosoWrote 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/hermoso-ai/hermoso/hermoso-product-photoshoot)<a href="https://agentmods.dev/skills/hermoso-ai/hermoso/hermoso-product-photoshoot"><img src="https://agentmods.dev/badge/skills/hermoso-ai/hermoso/hermoso-product-photoshoot/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/hermoso-ai/hermoso/hermoso-product-photoshoot"><img src="https://agentmods.dev/badge/skills/hermoso-ai/hermoso/hermoso-product-photoshoot.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 | $0.00105 | $0.00654 |
| Opus 5 | $0.00053 | $0.00327 |
| Sonnet 5 | $0.00021 | $0.00131 |
| Haiku 4.5 | $0.00011 | $0.00065 |
Grade A, and why
hermoso-product-photoshoot 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 12d 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
Hermoso — product photoshoot
Use Hermoso's reference-image compositing so the real product (label, colours, shape) is preserved while the scene around it is generated. Drive the Hermoso CLI.
Setup
hermoso auth login(opens your browser once; nothing to paste). On a machine with no browser:hermoso auth login --token <your key>, using a key from the app under MCP & CLI.; runhermoso capabilitiesonce to see image model ids + recipes. No account at all? An agent can sign itself up on a paid plan withPOST /v1/signupat app.hermoso.ai, no browser needed; see the Hermoso README.
Procedure
- Get the product image path/URL from the user. This is the
--ref— it forces product-accurate compositing. - Pick a mode and write the prompt accordingly (compose for ads — off-center hero, depth, directional light, negative space for copy):
product_shot— clean studio packshot on a seamless backdrop.lifestyle_scene— the product in a real in-use setting (kitchen, desk, outdoors).hero_banner— wide banner composition with room for a headline.social_carousel— several angles/scenes of the same product (run the command N times with varied prompts).ad_creative_pack— a few finished ad images with on-image copy.
- Render:
hermoso generate image --prompt "<scene + composition + any on-image text>" --ref <product image> [--model <id>] [--aspect 1:1|4:5|16:9]- Prefer the
★bestimage model for hero work; a faster model is fine for bulk variations.
- Prefer the
- For a pack, loop: vary the scene/angle/aspect per call, collect the URLs, and present them together.
- Report each served image URL; offer to
hermoso fetchthem to disk or to tweak a scene.
Notes
- Keep the product as the hero; never let generated text garble the real label — describe the product as "the exact product in the reference, label unchanged."
--reftakes a local path (read + sent) or a URL.
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.
- 12d ago First seen · 36 lines · 105 tokens per session scan A 45a978af01d0
hermoso-product-photoshoot is a skill published in the GitHub repository hermoso-ai/hermoso (0 stars, last pushed today), licensed MIT. It adds 105 tokens to every session and 654 once invoked, about $0.0005 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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