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-generategit 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-generate)<a href="https://agentmods.dev/skills/hermoso-ai/hermoso/hermoso-generate"><img src="https://agentmods.dev/badge/skills/hermoso-ai/hermoso/hermoso-generate/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-generate"><img src="https://agentmods.dev/badge/skills/hermoso-ai/hermoso/hermoso-generate.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 | $0.00118 | $0.00795 |
| Opus 5 | $0.00059 | $0.00398 |
| Sonnet 5 | $0.00024 | $0.00159 |
| Haiku 4.5 | $0.00012 | $0.00080 |
Grade A, and why
hermoso-generate 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.
How it starts
The opening of the file, as written. The whole thing — 36 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hermoso — generate ad creative
You drive the Hermoso CLI (hermoso) to render images and videos. Always report the final media URL.
Setup (once)
- Ensure the CLI is available. From the Hermoso repo:
node bin/hermoso.mjs version(orhermoso versionif globally installed vianpm i -g). 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. 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
- Always run
hermoso capabilitiesfirst. It lists the valid image/video model ids, their credit costs, aspect ratios, video durations, and the recipe ids. Never guess a model id. - Pick a high-quality default (quality over cost is the house rule): for images prefer the model marked
★best(e.g.nano-banana-pro); for product composites pass the real product image with--ref. For video, use a featured model and a sensible duration. - Generate:
- Image:
hermoso generate image --prompt "<full prompt incl. any on-image text>" [--ref ./product.png] [--model <id>] [--aspect 1:1] - Video:
hermoso generate video --prompt "<shot description>" [--ref ./frame.png] [--duration 8] [--aspect 9:16] [--model <id>] [--tts "<voiceover>"] [--voice Rachel] --wait - Avatar (lip-sync):
hermoso generate avatar --image ./face.png --script "<words>" [--voice George] --wait - Stitch (≥2 scenes):
hermoso generate stitch --scenes scenes.json --wait
- Image:
- Video/avatar/stitch are job-based — keep
--wait(default) so the command blocks and prints the final URL. If you don't wait, poll withhermoso jobs get <id> --wait. - Report the served URL (e.g.
https://assets.hermoso.ai/…), never a raw job id. If the user wants the file,hermoso fetch <url> --out name.png.
Notes
--refaccepts local file paths (read + sent as data) or URLs; a real product/logo ref makes the output product-accurate.- Add
--jsonto any command for machine-readable output. - If a render fails or times out, the error explains why; surface it plainly and offer to retry or pick a cheaper/faster model from
hermoso capabilities.
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 · 36 lines · 118 tokens per session scan A fb7af6a3c1f8
hermoso-generate is a skill published in the GitHub repository hermoso-ai/hermoso (0 stars, last pushed 2d ago), licensed MIT. It adds 118 tokens to every session and 795 once invoked, about $0.0006 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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