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 ad-policy-checkgit 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/ad-policy-check)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/ad-policy-check"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/ad-policy-check/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/ad-policy-check"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/ad-policy-check.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 | $0.00128 | $0.01563 |
| Opus 5.5 | $0.00051 | $0.00625 |
| Sonnet 5.5 | $0.00026 | $0.00313 |
| Haiku 4.5 | $0.00013 | $0.00156 |
Grade A, and why
ad-policy-check 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 4d 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ad-policy-check
Used by: make-static-ads (paid ads) and run-paid-campaigns (creative it checks at launch). Organic, email and creator content: load disclosure.md instead (or as well, when a paid ad uses a creator).
Craft knowledge, not legal advice. Policies change: when a rule decides whether an ad ships, tell the human to confirm it on the platform's live policy page. Claims are a separate check (the claims-gate skill, ../claims-gate/SKILL.md); run that first — a line cut there is not reworded here.
Reads: the ad's planned or rendered text (copy, on-image or on-screen words, end cards, captions), what the picture shows, the people in it, the platforms and placements from the brief, the brand's category, and the brand's dont / must learnings (past disapprovals).
Writes: one policy line per ad and platform, and any set-level flag.
1. Category first
Regulated (health, supplements, cosmetics with efficacy language, alcohol, cannabis/CBD, tobacco/vape, financial products, crypto, weight loss, kids' products, regulated professions, gambling, dating, political) or a special ad category on Meta (housing, employment, credit, social issues)?
- No brand rule on file for it → flag the whole set "needs legal review before launch".
- A special ad category → write it in the hand-off so whoever launches sets the flag; copy may not target protected traits.
- Gambling, dating, political → most platforms need pre-authorisation: flag and stop.
- Market matters (melatonin is prescription-only in the UK and Australia; the EU allows only authorised health-claim wordings; Canada needs licensed claims) — know the market before writing.
| Category | Rule of thumb |
|---|---|
| Health, supplements | Structure/function wording only with substantiation ("supports restful sleep"); never disease language ("treats", "cures", "for insomnia", "anxiety relief") |
| Cosmetics with efficacy | "Treats acne", "heals", "boosts collagen" are drug-like; approved wording only |
| Alcohol, cannabis/CBD, tobacco/vape | Age-gated; often banned or pre-approved; no health benefits |
| Financial products, crypto | Certification, risk disclosures, no guaranteed returns |
| Weight loss | No before/after, no time-bound promises; banned on Pinterest |
| Kids' products | No direct appeals to children; no children in melatonin ads, no "candy"/"treat" |
| Regulated professions | Local rules (e.g. no patient testimonials for some medical services) |
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.
- 4d ago First seen · 79 lines · 128 tokens per session scan A f028048b5a93
ad-policy-check is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,265 stars, last pushed yesterday), licensed MIT. It adds 128 tokens to every session and 1,563 once invoked, about $0.0005 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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