threejs-rain-snow-and-wet-surfaces

threejs-rain-snow-and-wet-surfaces is a skill for Codex from linegel/threejs-complete-set-of-skill. It costs 56 tokens per session (2,015 once invoked), scanned A, original, ISC.

A set of instructions for adding rain, snow, and wet-surface effects to Three.js, a JavaScript 3D graphics library. Airborne precipitation and surface deposits are treated as related but separately managed states.

In plain words
What is it for?
Use it for falling rain or snow, persistent accumulation, wet surfaces, weather-driven ripples, melting or evaporation, and impact splashes.
Why use it?
It helps keep weather appearance consistent with wind, temperature, humidity, deposited snow, wetness, ripples, and splashes.

Skill for Codex

Written for Codex: agents/openai.yaml present. Also seen: $skill-name invocation.

Good fit Use it for falling rain or snow, persistent accumulation, wet surfaces, weather-driven ripples, melting or evaporation, and impact splashes.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/linegel/threejs-complete-set-of-skill/threejs-rain-snow-and-wet-surfaces
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 linegel/threejs-complete-set-of-skill --skill threejs-rain-snow-and-wet-surfaces
Clone the repo
git clone --depth 1 https://github.com/linegel/threejs-complete-set-of-skill

Made for: 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 threejs-rain-snow-and-wet-surfaces

README.md
[![agentmods](https://agentmods.dev/badge/skills/linegel/threejs-complete-set-of-skill/threejs-rain-snow-and-wet-surfaces/github.svg)](https://agentmods.dev/skills/linegel/threejs-complete-set-of-skill/threejs-rain-snow-and-wet-surfaces)
Your own site
<a href="https://agentmods.dev/skills/linegel/threejs-complete-set-of-skill/threejs-rain-snow-and-wet-surfaces"><img src="https://agentmods.dev/badge/skills/linegel/threejs-complete-set-of-skill/threejs-rain-snow-and-wet-surfaces/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 threejs-rain-snow-and-wet-surfaces

Your own site · 80×15
<a href="https://agentmods.dev/skills/linegel/threejs-complete-set-of-skill/threejs-rain-snow-and-wet-surfaces"><img src="https://agentmods.dev/badge/skills/linegel/threejs-complete-set-of-skill/threejs-rain-snow-and-wet-surfaces.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 56 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,015 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.
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 $0.00056 $0.02015
Opus 5.5 $0.00022 $0.00806
Sonnet 5.5 $0.00011 $0.00403
Haiku 4.5 $0.00006 $0.00201

Measured 1mo ago against content hash 40c14445acfd, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

threejs-rain-snow-and-wet-surfaces 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 1mo ago.

The scan reads SKILL.md. This mod also ships 1 executable file (examples/physical-area-deposition.mjs), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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/threejs-rain-snow-and-wet-surfaces/SKILL.md · 201 lines

How it starts

The opening of the file, as written. The whole thing — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Rain, Snow, and Wet Surfaces

Couple appearance to causes: one time source and wind field drive airborne precipitation, while one owner per receiver integrates deposited rain or snow. Melt/refreeze transfer equal mass between liquid and snow; neither creates inventory. Visual particle count samples the weather; it never sets the deposited mass.

Use Three.js r185 WebGPURenderer, TSL, storage nodes, node materials, and the node post stack. Initialize the renderer before allocating compute or storage:

await renderer.init();

if (renderer.backend.isWebGPUBackend !== true) {
  throw new Error('This weather path requires the WebGPU backend.');
}

For cross-system precipitation or receiver state, declare its units, frame, time interval, authority/version, support, validity, and reset semantics; invoke $threejs-choose-skills when ownership spans skills.

Build sequence

1. Name the owners and units

Declare:

  • one monotonically sampled time source and update interval in seconds;
  • one air-velocity field in world metres per second, including its frame, support, cadence, and validity;
  • temperature in kelvin and a named humidity convention when phase, melt, or evaporation depends on them;
  • precipitation forcing as liquid/ice mass-area flux in kg m^-2 s^-1 over a physical receiver support;
  • one receiver-state owner for each liquid or snow inventory;
  • one owner for HDR presentation, tone mapping, and output conversion.

Treat cloud appearance and causal precipitation as separate branches. An appearance-only cloud may coordinate art direction. A causal cloud source publishes a mass flux or airborne inventory with a fall-delay/transport model; rain transports it to receivers on a later ordered stage.

This step is complete when every cause and persistent state has exactly one owner, every exchanged quantity has units and a frame, and every producer is sampled within its stated validity.

2. Select motion before allocating state

Use immutable seeds and analytic vertex motion when position is an exact function of seed, time, and integrated wind. Use recurrent GPU-resident state when turbulence, collisions, feedback, or path history affects the next state. Authored time-varying wind remains analytic only when its displacement integral is available; multiplying the current wind by total elapsed time makes all particles jump when wind changes.

Read the full file on GitHub · 201 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. 1mo ago Changed · +4 lines 40c14445acfd
  2. 2mo ago First seen · 197 lines · 56 tokens per session scan A a318aa933ac3

Subscribe to this mod's changes

threejs-rain-snow-and-wet-surfaces is a skill published in the GitHub repository linegel/threejs-complete-set-of-skill (11 stars, last pushed 1mo ago), licensed ISC. It adds 56 tokens to every session and 2,015 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.