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 vucinatim/air-jam --skill r3f-3d-scenegit clone --depth 1 https://github.com/vucinatim/air-jamWrote 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/vucinatim/air-jam/r3f-3d-scene)<a href="https://agentmods.dev/skills/vucinatim/air-jam/r3f-3d-scene"><img src="https://agentmods.dev/badge/skills/vucinatim/air-jam/r3f-3d-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/vucinatim/air-jam/r3f-3d-scene"><img src="https://agentmods.dev/badge/skills/vucinatim/air-jam/r3f-3d-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 | $0.00050 | $0.00397 |
| Opus 5 | $0.00025 | $0.00198 |
| Sonnet 5 | $0.00010 | $0.00079 |
| Haiku 4.5 | $0.00005 | $0.00040 |
Grade A, and why
r3f-3d-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 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.
What it actually says
R3F 3D Scene
Use this skill for 3D host gameplay surfaces built with React Three Fiber and Three.js.
Read First
docs/airjam/r3f-3d-scene.mddocs/airjam/generated/state-and-rendering.mddocs/airjam/generated/project-structure.md
Core Rule
Treat R3F as a rendering layer.
Keep:
- gameplay rules in domain/system modules
- runtime orchestration in engine/adapters
- scene composition and visual presentation in the 3D layer
Scene Quality Rules
- establish a deliberate lighting recipe early
- define world origin, ground level, and object placement conventions clearly
- keep shadows intentionally tuned to the actual play area
- use particles and post effects as accents, not camouflage for weak art direction
Physics Rule
Use Rapier when the game really benefits from rigid-body style simulation, collision response, or stacked interactions.
If gameplay only needs simple movement, overlap checks, or authored responses, prefer a lighter custom movement/collision model.
Asset Rule
- keep model scale and grounding consistent
- prefer curated materials and textures over random defaults
- use custom shaders only when they materially improve the look or mechanic
Anti-Patterns
- React state driving per-frame scene simulation
- giant untuned shadow maps with clipped or blurry shadows
- floating models fixed by random per-scene offsets
- adding a full physics engine where simple authored logic would be clearer
- piling on particles, bloom, or shaders before the base scene reads well
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 · 52 lines · 50 tokens per session scan A b2497b437fe7
r3f-3d-scene is a skill published in the GitHub repository vucinatim/air-jam (6 stars, last pushed 2d ago), licensed MIT. It adds 50 tokens to every session and 397 once invoked, about $0.0003 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 2026-08-31.
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