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-scalable-real-time-shadowsgit 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-scalable-real-time-shadows)<a href="https://agentmods.dev/skills/linegel/threejs-complete-set-of-skill/threejs-scalable-real-time-shadows"><img src="https://agentmods.dev/badge/skills/linegel/threejs-complete-set-of-skill/threejs-scalable-real-time-shadows/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-scalable-real-time-shadows"><img src="https://agentmods.dev/badge/skills/linegel/threejs-complete-set-of-skill/threejs-scalable-real-time-shadows.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 | $0.00066 | $0.01947 |
| Opus 5 | $0.00033 | $0.00974 |
| Sonnet 5 | $0.00013 | $0.00389 |
| Haiku 4.5 | $0.00007 | $0.00195 |
Grade A, and why
threejs-scalable-real-time-shadows 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 12d 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 — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scalable Real-Time Shadows
Directional shadows are a fit problem: fit the smallest light-space representation that covers required caster-to-receiver rays, then spend work on the projection, filter, and updates receivers actually use.
$threejs-choose-skills is an optional multi-system coordinator. Use
$threejs-visual-validation for capture/readback evidence.
1. Choose one topology
Compare the same seeded workload, projection, filter, and receiver policy:
| Gate | First r185 WebGPU branch | Cost topology |
|---|---|---|
| A bounded receiver/caster volume passes world-texel and depth-precision gates | one DirectionalLight shadow |
one view, one depth texture, one filter evaluation |
| Camera-depth coverage changes continuously | CSMShadowNode |
L shadow views/maps; normally one active filter, two in a fade |
| One fixed orthographic footprint is partitioned spatially | TileShadowNode |
one depth array, N backend layer passes, N containment branches, and a union render list that can draw each caster in every layer |
| Very large coverage persists and measured reuse survives invalidation | custom cached clipmap | L persistent levels; selected updates; L portable filters or one/two target-proven array/atlas filters |
| Casters deform or change broadly each frame | one fitted shadow, CSM, or a same-light static/dynamic split | Cache invalidation approaches full redraw. |
Core and Tile shadow targets retain color-bearing attachments beside depth; VSM
adds distribution/blur resources and passes. TileShadowNode updates all layers
together and is not a persistent tile cache.
Classify consequential numbers as Derived from bounds/equations, Authored intent, Gated limits, or target Measured evidence.
Complete when: one topology is selected from measured draw/raster/receiver/ binding/reuse pressure, every rejected branch has a failed gate, and no copied map size, level count, bias, or millisecond promise drives the choice.
What ships with it
3 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.
- 12d ago First seen · 174 lines · 66 tokens per session scan A f2f777d80169
threejs-scalable-real-time-shadows is a skill published in the GitHub repository linegel/threejs-complete-set-of-skill (6 stars, last pushed 1mo ago), licensed ISC. It adds 66 tokens to every session and 1,947 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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