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 render-airdrop-carouselgit 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/render-airdrop-carousel)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/render-airdrop-carousel"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-airdrop-carousel/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/render-airdrop-carousel"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-airdrop-carousel.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 | $0.00207 | $0.01415 |
| Opus 5.5 | $0.00083 | $0.00566 |
| Sonnet 5.5 | $0.00041 | $0.00283 |
| Haiku 4.5 | $0.00021 | $0.00142 |
Grade A, and why
render-airdrop-carousel 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 yesterday.
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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
render-airdrop-carousel
The free, deterministic renderer for the airdrop-notification-carousel video ad format — the viral iOS "AirDrop" trend where a native share-sheet card ("Brand would like to share a candle · Decline / Accept") springs up on the phone and its preview window flips through a carousel of real product photos, landing on a range/lineup payoff with an Accept tap.
This is a DETERMINISTIC composite — no generative video, no AI-rendered product.
A real-DOM AirDrop card is rendered once to PNG (headless Chrome), chroma-keyed, and
its preview window is refilled per-product in PIL, then animated + audio-synthed with
FFmpeg. The whole point of the format is crisp system-UI text and real product
photography, both of which a video model would smear. This capability makes no paid
calls; the recipe gates the only optional paid step — generating a hero product shot
when the brand has NO usable photo at all (→ create-image-fal).
Default output ≈ 6–8s, 1080×1920, h264 + aac. Duration is DERIVED, not trimmed —
first_hold + (N-1)·per + final_hold. Add images or raise per to lengthen.
Scripts (free)
scripts/build_card.py— brand params →chrome.html+chrome-pressed.html: the AirDrop card on a green page (#00e000) with a magenta preview window (#ff00ff) and a solid brand band (wordmark SVG or text + tagline). Real DOM text —AirDrop,Decline,Accept, and the brand line are DOM/SVG, never AI-rendered.scripts/one_shot.py— glue:build_card→ headless-Chrome (Playwright) fullPage screenshot of both card states →compose_carousel. One--config, one MP4.scripts/compose_carousel.py— the render engine: green-key the card → detect the magenta window → fill it per-product (cover-crop) → blurred per-product backdrop + push-in → card spring-up (iOS ease-out-back) + carousel + Accept tap → synth audio (whoosh on entry, chime on land, a tick per swap, a pop on the tap) → encode h264+aac.scripts/config.example.json— the shape of the brandconfigthe recipe binds (brand-neutral worked defaults; replace every/abs/path/...placeholder).
What ships with it
6 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.
- yesterday Changed f14a959f1a84
- 28d ago Changed · +9 lines fd3ce6f0c9f0
- 1mo ago First seen · 72 lines · 207 tokens per session scan A 40e36f8c0b4b
render-airdrop-carousel is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,270 stars, last pushed yesterday), licensed MIT. It adds 207 tokens to every session and 1,415 once invoked, about $0.0008 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 2026-08-30.
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