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-cgi-sizzlegit 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-cgi-sizzle)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/render-cgi-sizzle"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-cgi-sizzle/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-cgi-sizzle"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-cgi-sizzle.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 | $0.00208 | $0.01443 |
| Opus 5.5 | $0.00083 | $0.00577 |
| Sonnet 5.5 | $0.00042 | $0.00289 |
| Haiku 4.5 | $0.00021 | $0.00144 |
Grade A, and why
render-cgi-sizzle 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
render-cgi-sizzle
Assembles the 3D-CGI app sizzle video format: a phone floats in a CGI studio while five or six app features demo one per beat — each beat bursts REAL App Store UI elements out of the phone in 3D with rim-light + bokeh, then everything collapses back into the screen at a value-prop climax + a brand end card over a music bed. It reads as an Apple-keynote product film, not UGC and not a physical-product shoot.
The worked example (scripts/config.example.json) is the fictional trail-map app Trailvane: a gold phone in a
smoky-black studio with an amaranth rim-light, an "Eryn" keynote-cadence VO, a premium-tech
piano/synth bed, and an "Every trail worth walking. One app." climax. Those are one example, not defaults.
Choices
Creative calls the user makes (the recipe's choices, asked in one round before any paid
step). The demo's value is only an example.
studio_look— background, rim-light colour, bokeh; also the end-card canvas + bar. Asked of the user; the demo used smoky-black#0A0A0A+ amaranth#E32652rim-light.phone— the phone model/finish in every plate. Asked; the demo used a gold/champagne titanium iPhone 15 Pro.narrator_voice— the VO voice. Asked; the demo used ElevenLabs "Eryn".vo_delivery— how the VO is written and read. Asked; the demo used a restrained Apple-keynote spec-sheet cadence.music— the bed's genre/mood. Asked; the demo used a premium-tech underscore.
This capability ships the recipe (scripts/config.example.json) + a config→step map
(scripts/PIPELINE.md) + the FREE-assembly how-to (scripts/README.md). It documents the
FREE, deterministic assembly between the paid model calls:
- nano-banana CGI plates (paid, separate cap) render only the phone shell + the chosen
studio + rim-light + bokeh + placeholder burst shapes; the screen is left a blank
warm glow on purpose, and every plate is
--anchored on beat 1 so the phone/studio stay identical across beats. - PIL real-UI compositing (FREE) — auto-detect the bright phone-screen bbox in each
plate and feather the REAL App Store screenshot into the bezel →
scene-NN-composite.png; bake real burst-out overlays (e.g. climax portrait tiles, rim-light baked before rotation) around the plate. The on-screen UI + faces + wordmark are ALWAYS real assets — never AI-rendered, so no claim is invented and nothing reads fake. - Kling 3.0 i2v steady-float (paid, separate cap) drives each composite; the burst-out
pops/settles while the phone + screen stay locked. Any beat Kling garbles drops to a FREE
Ken-Burns FFmpeg push-in (
zoompan, heavier on the climax) — the shipped demo used this path for the feature beats. - Assembly + finalize (FREE) — dice + intercut concat (timeline locked from the measured VO durations), audio mix (sidechain-duck the music under VO, loudnorm -14 LUFS master), PIL brand end card (real wordmark, never AI), then 1.15x speed + anti-AI grain 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.
- today Changed 0ae5f2ccae86
- 26d ago Changed · +18 lines · +11 tokens per session b5028006e8a1
- 1mo ago First seen · 66 lines · 197 tokens per session scan A 778809a1e188
render-cgi-sizzle is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,265 stars, last pushed yesterday), licensed MIT. It adds 208 tokens to every session and 1,443 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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