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 product-photoshootgit 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/product-photoshoot)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/product-photoshoot"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/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/gooseworks-ai/goose-skills/product-photoshoot"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/product-photoshoot.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00035 | $0.00477 |
| Opus 5 | $0.00017 | $0.00238 |
| Sonnet 5 | $0.00007 | $0.00095 |
| Haiku 4.5 | $0.00003 | $0.00048 |
Grade A, and why
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 13d 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.
Product Photoshoot
Turn real catalog images into publish-ready product photography while preserving silhouette, materials, logo, packaging, and colorway. The GooseWorks backend uses the same generation, fidelity review, and retry pipeline as its Product Photos studio.
Prerequisite
This workflow requires the GooseWorks MCP tools. If they are unavailable, tell the user how to install the GooseWorks MCP connection and stop before generation.
Workflow
- Resolve the brand with
list_ad_brandsand the product withlist_brand_products. - If the product is missing, use
import_productwith a product URL, Shopify store, or public image URL. Pollget_product_import; do not re-submit an in-progress import. - Clarify the intended image: studio, lifestyle, on-model, close-up, setting, aspect, and count. Do not invent product attributes.
- Call
estimate_product_photosand show the user the credit estimate. Confirm count and quality before spending. - Call
generate_product_photoswith the chosen product, category, controls, count, quality, and optional prompt. Human model imagery requires the user's rights attestation. - Poll
get_product_photo_generationuntilcomplete,partial_failure, orfailed. Do not submit a duplicate while it is running. - Show every result and status. Let the user choose the keepers; use
approve_product_photoonly for selected results andarchive_product_photofor rejected ones.
Tool map
- Brand and catalog:
list_ad_brands,list_brand_products,import_product,get_product_import - Cost and generation:
estimate_product_photos,generate_product_photos,get_product_photo_generation - Results:
list_product_photos,approve_product_photo,archive_product_photo
Rules
- Ask before spending credits.
- Approved photos become reusable brand creative inputs; unapproved photos do not.
- A fidelity-flagged output may be shown for review but must not be described as approved.
- Never imply that a generated person is a real customer or spokesperson.
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.
- 13d ago First seen · 36 lines · 35 tokens per session scan A 606455bf7be7
product-photoshoot is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 35 tokens to every session and 477 once invoked, about $0.0002 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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