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 linegel/threejs-complete-set-of-skill --skill threejs-volumetric-cloudsgit clone --depth 1 https://github.com/linegel/threejs-complete-set-of-skillWrote 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/linegel/threejs-complete-set-of-skill/threejs-volumetric-clouds)<a href="https://agentmods.dev/skills/linegel/threejs-complete-set-of-skill/threejs-volumetric-clouds"><img src="https://agentmods.dev/badge/skills/linegel/threejs-complete-set-of-skill/threejs-volumetric-clouds/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/linegel/threejs-complete-set-of-skill/threejs-volumetric-clouds"><img src="https://agentmods.dev/badge/skills/linegel/threejs-complete-set-of-skill/threejs-volumetric-clouds.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 | $0.00049 | $0.02899 |
| Opus 5.5 | $0.00020 | $0.01160 |
| Sonnet 5.5 | $0.00010 | $0.00580 |
| Haiku 4.5 | $0.00005 | $0.00290 |
Grade A, and why
threejs-volumetric-clouds 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.
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 — 278 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Volumetric Clouds
Build a bounded volume whose broad mass comes from weather-scale causes, whose fine detail erodes that mass, and whose optical, shadow, and temporal errors are measurable.
Process
1. Select the claim and workload branches
State the claim first. Procedural weather, coverage, shape, and detail usually form an authored appearance model. Beer-Lambert attenuation is physical for the declared density and coefficients. Dual-lobe phase fits, octave multiple-scattering compensation, powder, and simple ground bounce remain approximations until validated against a transport reference.
Select each independent workload branch:
| Decision | Select | Evidence |
|---|---|---|
| Local versus broad | full-resolution scissored march for a small projected bound; reduced-resolution march for broad coverage | complete-branch GPU cost and image error |
| Full versus reduced current grid | full current grid for low reuse; reduced grid plus reconstruction for coherent broad clouds | current-sample, bandwidth, and reconstruction error |
| Dense versus sparse | bounded adaptive march for dense occupancy; conservative macrocell DDA for sparse occupancy | saved samples exceed hierarchy build/traversal cost |
| Receiver shadow | none when no external receiver queries cloud shadow; full-column 2D optical depth for ground/opaque receivers; short sun march or depth-aware light product for in-cloud samples | admitted receiver query and transmittance error |
| Precipitation | appearance-only cues; or causal liquid/ice emission consumed by $threejs-rain-snow-and-wet-surfaces |
dimensioned emission, support, transport delay, and conservation error |
For causal precipitation, publish liquid and ice mass flux in kg m^-2 s^-1,
or interval-integrated areal mass in kg m^-2, explicitly identified with its
sample time, sample interval, physics frame/origin, physical support,
area/Jacobian convention, fall delay or transport model, owner, contract
version, generation, validity, conservation gate, and error. Publish each
interval once to $threejs-rain-snow-and-wet-surfaces. The receiver converts
flux to interval mass once, preserves the declared support/Jacobian, and applies
the declared delay or transport before accumulation.
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.
- 1mo ago Changed · +3 lines c3f4c66d7d86
- 2mo ago First seen · 275 lines · 49 tokens per session scan A 8651d941ac95
threejs-volumetric-clouds is a skill published in the GitHub repository linegel/threejs-complete-set-of-skill (11 stars, last pushed 1mo ago), licensed ISC. It adds 49 tokens to every session and 2,899 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.
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