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 gooseworksgit 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/gooseworks)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/gooseworks"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/gooseworks/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/gooseworks"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/gooseworks.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 | $0.00070 | $0.01760 |
| Opus 5.5 | $0.00028 | $0.00704 |
| Sonnet 5.5 | $0.00014 | $0.00352 |
| Haiku 4.5 | $0.00007 | $0.00176 |
Grade A, and why
gooseworks 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GooseWorks
Route specialist jobs to their skills; use GooseWorks for web and social data.
First steps
- Discover the listed setup/status action and read brand/task state. Complete missing facts; preserve the selected brand/task and continue once setup is done.
- Read brand_read and advertised knowledge actions for rules/evidence; pass results to the playbook. Use legacy action names only when listed; ask only what remains ambiguous.
- Save lasting human brand facts in their words through the listed typed knowledge action. Keep guesses and temporary creative choices as proposals; follow claim, rights and approval rules. Generic context is not approved product proof.
Skill routes
| If the user wants… | Route to | How |
|---|---|---|
| Raw social post lists, engagement rankings or voices lists | marketing-data-access |
Fetch marketing-data-access and follow focused-reads.md. |
| Learn a reusable brand or author voice from blog posts or social writing | voice-card |
Fetch it with catalog_fetch, or gooseworks fetch voice-card in a terminal. |
| Static/image ads, including campaigns, more ads, winner variants, copy/font changes or rejected-image fixes | make-static-ads |
Fetch with catalog_fetch or gooseworks fetch; follow selection/review/quote gates. Stop creation if unavailable. |
Change one ad already made in GooseWorks (an edit, a new size, its tags or rating), or save the customer's image as a template. New image ads: make-static-ads |
goose-ads |
Open it with catalog_fetch in a chat app, or the installed copy in a terminal. |
| Charts, infographics, slides, social graphics, branded visual designs from a style or format | goose-graphics |
Fetch it with catalog_fetch, or gooseworks fetch goose-graphics in a terminal. |
| Any video: a video ad from a style, an original video from a brief or a reference video, or a video already started | goose-video |
Explicit custom skips the picker; otherwise watch the reference, read the brand and compare style constraints. |
| Make product photos: studio, lifestyle, marketplace, social or on a model | goose-product-photos |
Open it with catalog_fetch in a chat app, or the installed copy in a terminal. |
| Animate an approved static ad or product image | animate-image |
Fetch it with catalog_fetch, or gooseworks fetch animate-image in a terminal. |
| Anything else: find a skill or a single step | (stay here) | catalog_search, or gooseworks search in a terminal; see "Data work". |
What ships with it
2 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 First seen · 104 lines · 70 tokens per session scan A 16ab567685f4
gooseworks is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,265 stars, last pushed yesterday), licensed MIT. It adds 70 tokens to every session and 1,760 once invoked, about $0.0003 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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