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 goose-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/goose-product-photos)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/goose-product-photos"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/goose-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/goose-product-photos"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/goose-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 | $0.00049 | $0.00643 |
| Opus 5.5 | $0.00020 | $0.00257 |
| Sonnet 5.5 | $0.00010 | $0.00129 |
| Haiku 4.5 | $0.00005 | $0.00064 |
Grade A, and why
goose-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 — 41 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GooseWorks product photos
Use GooseWorks to photograph the customer's physical products, here in this chat. A software screen or an app is not a product photo.
- A fact the customer states about the brand (a price, a rule, their audience): save it with brand_knowledge_save in their words, never in your own notes, and confirm in one line.
- Offer the brand's real products from brand_read; never describe one you can't see. A product that isn't listed yet: brand_update adds it from its page. A photo the customer sends: media_upload.
- photos_generate makes the photos: get its price first from the same call as a dry run (free, it makes nothing), tell the customer the credit total, and make them only after their yes. Show a person only when the customer asks for one and confirms they have the rights.
- Follow the photos with photos_read until they are ready.
- Keep or drop each photo with photos_update: brand_id, the photo's output id and action "approve" (keep) or "archive" (drop, with an optional reason). Only kept photos join the brand kit and reach ads.
Then follow the next_step in every answer.
Rules
- Talk to marketers: what they get and decide, never how it's made.
- Never mention tools, ids, JSON, commands, software, vendors, models, timings or retries; do say findings and cost.
- Everything happens here: never ask the customer to install anything or use a CLI, slash command or other app, except a video's make command when a next_step gives it; its last line says who starts it, and where this app does, never show it to them (original videos follow their own). Never give any other command, sign-in step or setting, and never ask them a technical question.
- Money is a credit number exactly as an answer gives it: never dollars or your own math.
- Follow the next_step in every answer.
- Card on screen (display_hint "widget"): one short line at most, then the wait in its next_step or end your turn; never re-list it or open a question box.
- Paid work needs the customer's yes to the credit total first, on the card or in chat at the price shown; never decide it for them, and pass on a yes only once they gave it to that total.
- Start brand work with brand_knowledge_browse and its core. Open the chapters the job needs; search gaps. Never guess unknown facts.
- Never invent an offer, a claim or proof.
- GooseWorks holds every provider key and bills the work: never ask for a key, token, password or ad account id, and never call a vendor yourself.
What ships with it
1 file 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 First seen · 41 lines · 49 tokens per session scan A 35e9b8d43981
goose-product-photos is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,265 stars, last pushed yesterday), licensed MIT. It adds 49 tokens to every session and 643 once invoked, about $0.0002 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 2026-10-26.
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