Goose Skills is a library of workflows and data APIs that lets coding agents handle growth and go-to-market work such as advertising, social media, content, SEO, lead generation, and customer research. It is intended for teams using Claude Code, Cursor, Codex, and similar agents. The catalogue entries are its reusable skills.
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 gooseworks-ai/goose-skills --skill make-product-photosgit clone --depth 1 https://github.com/gooseworks-ai/goose-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/gooseworks-ai/goose-skills/make-product-photos)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/make-product-photos"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/make-product-photos/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/gooseworks-ai/goose-skills/make-product-photos"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/make-product-photos.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 | $0.00218 | $0.03749 |
| Opus 5.5 | $0.00087 | $0.01500 |
| Sonnet 5.5 | $0.00044 | $0.00750 |
| Haiku 4.5 | $0.00022 | $0.00375 |
Grade A, and why
make-product-photos 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 today.
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 — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
make-product-photos
The engine composes, generates, judges and retries. This playbook is the e-commerce creative director around it: it decides which pictures the page needs, curates which real photos the engine sees, turns each shot into a precise prompt, holds every scene to what the product physically is, checks each output against the real product, and keeps the catalogue looking like one shoot.
What you get
- A shot list from the surface (store page, ads, social, email, launch page), each shot with its slot, composition, light, scene and ratio — agreed before anything is made.
- A house-style card derived from the brand's existing photos (when they can be seen), confirmed once and saved.
- The photo set as the page it fills: each photo with its use, the engine's status and score, this playbook's verdict (keep / maybe / reject + named reason), and who checked it (by eye, or engine only with a checklist for you).
- "What we could not make and why", and the credits actually charged.
Before you start
Hard rules:
- Honesty: a scene may show only what the product physically is and does — its size, contents, finish, colour and how it is used — from the product record, the real photos and the human's confirmation. Never a result unless it is a signed-off claim (
../../../skills/marketing/claims-gate/SKILL.md), never a feature it lacks (references/fit-and-honesty.md§2 is the one list). - References: only real photos of this exact product, size and colour; never text graphics, other sizes, duplicates or earlier generated images.
- Money: nothing generates before the marketer's yes to the engine's quotes, shown one by one exactly as given — never your own sum (
../../../skills/marketing/spend-gate/SKILL.md, branch A); the hero runs first; a re-shoot is quoted again. - People: any shot with a person or a body part (hands included) needs the human's own attestation; no real likeness; no children.
- Keepers: only the human's picks are approved into the brand kit; nothing is uploaded to a store.
- Honest review (
../../../skills/marketing/honest-review/SKILL.md) and plain words: never name a playbook, tool, id, model or vendor to the marketer.
What ships with it
8 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.
- today Changed 7da32564d396
- 5d ago First seen · 102 lines · 218 tokens per session scan A 10c16b2b25d1
make-product-photos is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,265 stars, last pushed today), licensed MIT. It adds 218 tokens to every session and 3,749 once invoked, about $0.0009 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 2026-10-21.
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