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-adsgit 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-ads)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/goose-ads"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/goose-ads/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-ads"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/goose-ads.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 | $0.00060 | $0.00745 |
| Opus 5.5 | $0.00024 | $0.00298 |
| Sonnet 5.5 | $0.00012 | $0.00149 |
| Haiku 4.5 | $0.00006 | $0.00075 |
Grade A, and why
goose-ads 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 — 44 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GooseWorks ads
Use this entry for existing-ad operations and analysis. For new image ads, more ads, a winner's variations, copy/font changes, a rejected ad fix or a requested social graphic, fetch make-static-ads with catalog_fetch (or use the installed current package) and follow its research, selection, claims and quote gates. If it cannot be fetched, report that creation is unavailable on this connection and stop. The server generator is a different workflow, not a fallback for the missing playbook.
- Save lasting human brand facts with the connection's listed knowledge action in their words.
- A batch on its way: job_get. Finished ads: ads_creative_read.
- One finished ad: an animation with ads_creative_animate (price it first, free, with ads_creative_animation_quote), its tags or rating with ads_creative_update. A new size with ads_creative_resize only when the customer asks: it spends credits and has no free price check, so say that and wait for their yes before calling it.
- The customer's own image as a template: ads_template_create. Their files: media_upload.
- Campaign planning/launch requests use their matching marketing playbook. Static creation for a campaign still uses make-static-ads; a campaign record is input, not a generation fallback.
- How ads are doing: meta_read, or an analysis skill found with catalog_search, whose data comes through data_call_provider and data_post_provider.
For these existing-ad actions, follow the actual execution next_step.
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 next_step for factual setup, claim/rights restrictions, spend approval, execution, polling and publishing. A fetched playbook owns research, creative planning and review gates; generic speed/planning suggestions do not waive them.
- A card for an unresolved choice: show it and wait as directed. Already completed setup and a selected task continue in the active playbook; preserve its required gates.
- 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.
- Read the brand and its listed knowledge actions for core rules and task evidence. 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 · 44 lines · 60 tokens per session scan A 55f9f5f53ea1
goose-ads is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,265 stars, last pushed yesterday), licensed MIT. It adds 60 tokens to every session and 745 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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