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-3d-character-explainergit 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-3d-character-explainer)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/render-3d-character-explainer"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-3d-character-explainer/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-3d-character-explainer"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-3d-character-explainer.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 | $0.00178 | $0.02373 |
| Opus 5.5 | $0.00071 | $0.00949 |
| Sonnet 5.5 | $0.00036 | $0.00475 |
| Haiku 4.5 | $0.00018 | $0.00237 |
Grade A, and why
render-3d-character-explainer 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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
render-3d-character-explainer
The free, deterministic renderer for the 3d-character-explainer video ad format — an animated character spot (art style is the user's choice; the demo was glossy Pixar-style 3D) built on an "N types of X" listicle spine, where a recurring human protagonist plus a locked cast of N persona characters (one per list item) carry a hook → "deeper story" → cast-reveal → one beat per list item → kicker → product test → relieved payoff. This capability is the FREE assembly stage only. All generative work (Nano-Banana cast anchors + per-scene keyframes, Kling-V3 i2v clips, ElevenLabs VO + music, or a source ad's audio reused verbatim) happens upstream in the recipe and is handed to this capability as files.
It ports the validated compose recipe from the worked example — a "six types" Pixar-style 3D
listicle restyle (_render_full.sh — per-scene trim → normalize 1080×1920/fps30 → concat -c copy → mux the
source audio, with a static-still fallback on any failed clip). The assembly is
deterministic — iterate the cut for free, re-roll only the offending paid beat.
Choices
The recipe asks these of the user before any paid step; this capability assembles whatever comes back. None of them is fixed by the format.
visual_style— the art style of the anchors, keyframes and Kling motion prompts (Pixar-style 3D, claymation, anime, paper-cut, felt). Asked of the user; the demo used glossy Pixar-style 3D.narrator— who tells the story and in whose POV the lines / captions are written. Asked of the user; the demo used an off-screen storyteller.narrator_voice— the VO voice (ORIGINAL mode only). Asked of the user; the demo used one ElevenLabs narrator.tone— cute/comedic, sincere, educational, dramatic, punchy; shapes the style prefix, the delivery and the script. Asked of the user; the demo was cute and comedic.protagonist— the human's age, gender, look and outfit (locked via the world lock). Asked of the user; the demo used a young protagonist in a single tee.music— the bed under the VO, or none (ORIGINAL mode →music_bed). Asked of the user; the demo used a light instrumental underscore.
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.
- today Changed · -135 tokens per session ca2636aa42bb
- 26d ago Changed · +20 lines · +18 tokens per session 34f35f8aa200
- 1mo ago First seen · 108 lines · 295 tokens per session scan A 045c929fbf7a
render-3d-character-explainer is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,265 stars, last pushed yesterday), licensed MIT. It adds 178 tokens to every session and 2,373 once invoked, about $0.0007 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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