MengTo/Skills is a collection of reusable instruction folders that teach coding agents workflows for interface design, games, frontend systems, automations, and agent loops. Designers and developers use the skills to turn references, prompts, and implementation habits into repeatable work. The catalogue contains the project’s skills and supporting instruction.
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 MengTo/Skills --skill threejs-landscapegit clone --depth 1 https://github.com/MengTo/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/mengto/skills/threejs-landscape)<a href="https://agentmods.dev/skills/mengto/skills/threejs-landscape"><img src="https://agentmods.dev/badge/skills/mengto/skills/threejs-landscape/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/mengto/skills/threejs-landscape"><img src="https://agentmods.dev/badge/skills/mengto/skills/threejs-landscape.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00121 | $0.02253 |
| Opus 5 | $0.00060 | $0.01126 |
| Sonnet 5 | $0.00024 | $0.00451 |
| Haiku 4.5 | $0.00012 | $0.00225 |
Grade A, and why
threejs-landscape 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 5d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- threejs-landscape — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Three.js Landscape
A backdrop has one job: make the subject look like it is somewhere. Everything here is chosen so the landscape reads at a glance and then gets out of the way.
Reach for threejs-weather to put rain, storm or snow on top of it, and threejs-towers when the subject standing in it is architecture.
Ring the camera with a polar grid
Do not build a square heightfield. A long lens sees a narrow wedge, so a square grid spends most of its triangles behind the camera and still runs out of resolution at the horizon.
Sample on a polar grid centred under the camera, with radial rings that get further apart as they recede:
const AN = 900, RN = 52, R0 = 2.0, R1 = 700; // angular, radial, near, far
for (let r = 0; r <= RN; r++) {
const t = r / RN;
const rad = R0 + (R1 - R0) * Math.pow(t, 2.4); // dense near, sparse far
for (let a = 0; a < AN; a++) {
const th = a / AN * Math.PI * 2;
push(Math.cos(th) * rad, landH(x, z), Math.sin(th) * rad);
}
}
Two thousand triangles near the subject beat two hundred thousand spread evenly. pow(t, 2.4) is the whole trick: every ring covers roughly the same number of screen pixels.
Watch the winding. On a polar grid it is easy to wind every quad the wrong way and end up looking at the sky through the ground. Index as (a0, b1, b0, a0, a1, b1) and check by orbiting under the horizon once, deliberately, before you build anything else on top.
Warp the domain before you layer octaves
Plain fBm reads as crumpled paper. Warping the sample position with another noise field before you evaluate it is the single biggest step toward terrain that looks eroded:
function landH(x, z) {
const wx = x + fbm(x * 0.012, z * 0.012, 3) * 26; // domain warp
const wz = z + fbm(x * 0.012 + 41, z * 0.012 - 17, 3) * 26;
let h = fbm(wx * 0.0075, wz * 0.0075, 5) * 34; // broad landforms
h += ridged(wx * 0.021, wz * 0.021, 3) * 9; // ridge lines
return h;
}
Keep the analytic function separate from the mesh. Grass, stones, fog and anything else you scatter must sample the same landH, or they will float and sink.
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.
- 5d ago First seen · 152 lines · 121 tokens per session scan A fa361bdf1008
threejs-landscape is a skill published in the GitHub repository MengTo/Skills (5,868 stars, last pushed 10d ago), licensed MIT. It adds 121 tokens to every session and 2,253 once invoked, about $0.0006 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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