render-cgi-sizzle

render-cgi-sizzle is a skill for Claude Code, Codex from gooseworks-ai/goose-skills. It costs 197 tokens per session (1,140 once invoked), scanned A, original, MIT.

A video-assembly recipe for making a vertical 3D app advertisement with a phone, real App Store screenshots, feature demonstrations, and a brand ending. It combines prepared animation plates, screenshots, overlays, and audio.

In plain words
What is it for?
Use it to create a product film that demonstrates six app features, composites real App Store screens into a phone, adds burst effects, joins the beats, and finishes with a brand message.
Why use it?
It separates paid media generation from free compositing and assembly. This lets real app screens be placed into the phone while keeping the phone and studio look consistent across scenes.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to create a product film that demonstrates six app features, composites real App Store screens into a phone, adds burst effects, joins the beats, and finishes with a brand message.

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Install with agentmods
npx agentmods add skills/gooseworks-ai/goose-skills/render-cgi-sizzle
About the project

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.

gooseworks-ai/goose-skills · 1,201 stars · on GitHub · gooseworks.ai

Install

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.

Any agent
npx skills add gooseworks-ai/goose-skills --skill render-cgi-sizzle
Clone the repo
git clone --depth 1 https://github.com/gooseworks-ai/goose-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for render-cgi-sizzle

README.md
[![agentmods](https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-cgi-sizzle/github.svg)](https://agentmods.dev/skills/gooseworks-ai/goose-skills/render-cgi-sizzle)
Your own site
<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.

agentmods 80×15 button for render-cgi-sizzle

Your own site · 80×15
<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>
Per session 197 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,140 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00197 $0.01140
Opus 5 $0.00098 $0.00570
Sonnet 5 $0.00039 $0.00228
Haiku 4.5 $0.00020 $0.00114

Measured 11d ago against content hash 778809a1e188, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

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 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.

skills/ads/capabilities/render-cgi-sizzle/SKILL.md · 66 lines

How it starts

The opening of the file, as written. The whole thing — 66 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 gold-trimmed phone floats in a smoky-black studio while six app features demo one per beat — each beat bursts REAL App Store UI elements out of the phone in 3D with amaranth rim-light + bokeh, then everything collapses back into the screen at a climax ("200+ classes. One app.") + a brand end card over a premium-tech bed. It reads as an Apple-keynote product film, not UGC and not a physical-product shoot.

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 + smoky-black studio + amaranth 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 instructor 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.

See scripts/README.md for the full FREE-assembly detail and scripts/PIPELINE.md for the config-field → source-step map.

Read the full file on GitHub · 66 lines

Files

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.

Changes

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.

  1. 11d ago First seen · 66 lines · 197 tokens per session scan A 778809a1e188

Subscribe to this mod's changes

render-cgi-sizzle is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,201 stars, last pushed 10d ago), licensed MIT. It adds 197 tokens to every session and 1,140 once invoked, about $0.0010 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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