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 notque/vexjoy-agent --skill game-asset-generatorgit clone --depth 1 https://github.com/notque/vexjoy-agentWrote 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/notque/vexjoy-agent/game-asset-generator)<a href="https://agentmods.dev/skills/notque/vexjoy-agent/game-asset-generator"><img src="https://agentmods.dev/badge/skills/notque/vexjoy-agent/game-asset-generator/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/notque/vexjoy-agent/game-asset-generator"><img src="https://agentmods.dev/badge/skills/notque/vexjoy-agent/game-asset-generator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 81 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
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 | $0.00032 | $0.02749 |
| Opus 5 | $0.00016 | $0.01375 |
| Sonnet 5 | $0.00006 | $0.00550 |
| Haiku 4.5 | $0.00003 | $0.00275 |
Grade A, and why
game-asset-generator 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 8d 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 — 223 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Game Asset Generator Skill
Overview
This skill generates game-ready assets (3D models, Gaussian Splat environments, 2D sprites, images/textures) using AI APIs and free asset sources. It follows a three-phase workflow: DETECT the asset type -> GENERATE via the appropriate API or source -> INTEGRATE into the game. Only the relevant reference is loaded per task -- do not load all references upfront.
Scope: Use for AI-generated 3D models, world environments, pixel art sprites, concept art, textures, and sourcing free pre-built assets. Keep game engine scripting, physics, game loop logic, and shader authoring in threejs-builder after asset generation.
Phase 1: DETECT
Goal: Identify the asset type from the request and load the single corresponding reference.
Step 1: Classify the request
| Signal in request | Asset Type | Reference to load |
|---|---|---|
| "3D model", "character model", "generate model", GLB, mesh, rig, animate, humanoid | 3D Model | references/meshyai.md |
| "environment", "world", "scene background", "gaussian splat", "splat", volumetric | Environment | references/worldlabs.md |
| "sprite", "pixel art", "2D character", "tile", "tileset", canvas sprite | 2D Sprite | references/pixel-art-sprites.md |
| "image", "texture", "concept art", "icon", "generate image", chroma key | Image / Texture | references/fal-ai-image.md |
| No API key available, "free asset", "find model", "download asset", generation failed | Existing Assets | references/asset-sources.md |
If the request is ambiguous between 3D Model and Image, ask: "Do you need a 3D mesh (GLB file for a Three.js scene) or a 2D image/texture?"
Step 2: Check API key availability
Before calling any paid API, verify the required key exists:
grep -E "MESHY_API_KEY|WLT_API_KEY|FAL_KEY" ~/.env 2>/dev/null
If the required key is missing, the fallback chain applies -- load references/asset-sources.md alongside the primary reference.
What ships with it
8 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.
- references/asset-sources.md 7.9 KB
- references/fal-ai-image.md 7.1 KB
- references/meshyai.md 7.3 KB
- references/pixel-art-sprites.md 8.7 KB
- references/worldlabs.md 7.9 KB
- scripts/fal_queue_image_run.py 9.2 KB runs code
- scripts/meshy-generate.mjs 11 KB runs code
- scripts/optimize-glb.mjs 3.8 KB runs code
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.
- 8d ago First seen · 223 lines · 32 tokens per session scan A d75734dcae85
game-asset-generator is a skill published in the GitHub repository notque/vexjoy-agent (419 stars, last pushed 2d ago), licensed MIT. It adds 32 tokens to every session and 2,749 once invoked, about $0.0002 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 2026-09-03.
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