Borrowing it
Nothing to install: this file belongs to elemarin/renderoni. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/elemarin/renderoni/main/.agents/skills/prompt-to-scene/SKILL.mdgit clone --depth 1 https://github.com/elemarin/renderoniWrote 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/elemarin/renderoni/prompt-to-scene)<a href="https://agentmods.dev/skills/elemarin/renderoni/prompt-to-scene"><img src="https://agentmods.dev/badge/skills/elemarin/renderoni/prompt-to-scene/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/elemarin/renderoni/prompt-to-scene"><img src="https://agentmods.dev/badge/skills/elemarin/renderoni/prompt-to-scene.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00054 | $0.01918 |
| Opus 5.5 | $0.00022 | $0.00767 |
| Sonnet 5.5 | $0.00011 | $0.00384 |
| Haiku 4.5 | $0.00005 | $0.00192 |
Grade A, and why
prompt-to-scene 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 2mo 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 — 188 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt → image → img2threejs → Renderoni
Do not one-shot a whole game as a giant TypeScript file. Keep the agent context small: one prompt, one scene image, one inventory JSON, then reconstruct unique objects independently.
Upstream reconstruction skill: img2threejs.
Renderoni only mounts the resulting () => THREE.Group factories.
Token budget (hard rules)
- Keep the inventory JSON in context. Target < 800 tokens.
- Never paste img2threejs
SKILL.md, forge scripts, or generated factory source into the main game conversation after the factory is written to disk. - Reconstruct one unique
factorykey per isolated pass. Reuse the same factory for repeated props (10 trees = 1 factory). - Gameplay stays in Renderoni presets (
kccPlayer,sensor,body,light). Factories are visuals + collider hints only.
Pipeline
prompt
→ one scene image (optional but preferred)
→ compact SceneInventory JSON
→ unique factory list
→ img2threejs per factory (or fallback primitive)
→ mountSceneInventory(engine, inventory, factories)
→ add player / win-lose with presets
1. Inventory first
Write scene-inventory.json using SceneInventory from renderoni/scene:
{
"version": 1,
"prompt": "stone courtyard with crate, lantern, tree, well, coin",
"image": "refs/courtyard.png",
"seed": 42,
"elements": [
{
"id": "crate",
"factory": "woodCrate",
"kind": "prop",
"position": [-3, 0.55, -2],
"collider": { "shape": "box", "size": [1.1, 1.1, 1.1] }
}
]
}
kind: terrain | prop | actor | pickup | decor.
factory is a registry key, not source code.
2. Scene image (optional)
If the user has no image, generate one wide establishing shot of the whole
scene. Then list visible objects. Use imageRegion (normalized 0–1) only when
cropping helps reconstruction. Do not generate a separate image per blade of
grass.
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.
- 2mo ago First seen · 188 lines · 54 tokens per session scan A 80548c134a69
prompt-to-scene is a skill published in the GitHub repository elemarin/renderoni (3 stars, last pushed 1mo ago), licensed MIT. It adds 54 tokens to every session and 1,918 once invoked, about $0.0002 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 2026-08-31.
prompt-to-scene compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| prompt-to-sceneelemarin/renderoni | Skill | 3 | 1mo ago | MIT | A | 54 | Claude Code, Codex |
| unreal-enginealivirgo/Major-AI-Skills | Skill | 2 | today | MIT | A | 26 | Claude Code |
| godotalivirgo/Major-AI-Skills | Skill | 2 | today | MIT | A | 26 | Claude Code |
| soundclawiamlukethedev/Claw3D | Skill | 2.4k | 5d ago | MIT | A | 18 | Claude Code, Codex |
| task-manageriamlukethedev/Claw3D | Skill | 2.4k | 5d ago | MIT | A | 56 | Claude Code, Codex |
| todoiamlukethedev/Claw3D | Skill | 2.4k | 5d ago | MIT | A | 12 | Claude Code, Codex |
| self-evolvetettethu/VibeGame | Skill | 378 | yesterday | Apache-2.0 | A | 50 | Claude Code, Codex |