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-multiworldgit 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-multiworld)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/render-multiworld"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-multiworld/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-multiworld"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-multiworld.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 | $0.00122 | $0.01272 |
| Opus 5 | $0.00061 | $0.00636 |
| Sonnet 5 | $0.00024 | $0.00254 |
| Haiku 4.5 | $0.00012 | $0.00127 |
Grade A, and why
render-multiworld 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 11d 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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
render-multiworld
Assemble a silent, music-led "multi-world product tour" ad (≈27s, 9:16) — a tour of three distinct "third-place" worlds, one per product/scent, that lands on a Pinterest-style brand end card. Each world is a two-shot pair: a ~4.5s WIDE kinetic-calm ARRIVAL (the environment dominates, the bottle stays small) hard-cutting to a ~3.5s top-down MACRO product MOMENT (the sealed bottle nested with its botanical companion). Scent identity is carried by the world + botanical companion, not by bottle color. No VO, no captions in the scenes — one music bed carries the whole thing.
This capability is the FREE, deterministic assembler. The paid steps — the six per-world clips, the AI flat-lay end-card background, the ElevenLabs music bed — are separate capabilities (see the gap below for the clips); the recipe orchestrates and gates them.
Run
- Trim clips (FFmpeg, FREE) — trim each per-world clip to its
scene_grid[].duration_sec(arrival 4.5 / macro 3.5), re-encode to the master spec (720×1280, 24fps, yuv420p, scale+pad, audio stripped). Trimming the macro so the top-down portion dominates also hides any label misrender at the clip's upright tilt extreme. - End card (Playwright/HTML, FREE) — screenshot
end_card.htmlover the AI flat-lay BACKGROUND, then FFmpeg-encode to adwell_sec(3.0s) static clip. Headline ("FIND YOUR DAILY.", Inter 900), one handwritten Caveat scent label + hand-drawn SVG arrow per bottle, Playfair wordmark + URL. End-card text is HTML, NEVER AI-rendered — the AI step produces the background only. - Hard-cut concat (FFmpeg, FREE) — concat the six trimmed clips in scene order (S01 arrival → S02 macro → … → S06 macro) + the end-card clip. Hard cuts (no dissolves), normalized to one fps/codec first so concat-copy is safe.
- Music mux + web encode (FFmpeg, FREE) — mux the single instrumental bed onto the
silent concat with
afadein/out +loudnorm I=-16:TP=-1.5:LRA=11, AAC 192k, clamped to 27.0s, explicit single-audio map so no silent scene-track leaks in → the H.264 (+ AAC) 720×1280 master.
What ships with it
5 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.
- 11d ago First seen · 72 lines · 122 tokens per session scan A c5662e5690f8
render-multiworld is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,201 stars, last pushed 10d ago), licensed MIT. It adds 122 tokens to every session and 1,272 once invoked, about $0.0006 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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