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 scottstts/Threejs-Awesome-Graphics-Agent-Skills --skill threejs-raymarched-space-effectsgit clone --depth 1 https://github.com/scottstts/Threejs-Awesome-Graphics-Agent-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/scottstts/threejs-awesome-graphics-agent-skills/threejs-raymarched-space-effects)<a href="https://agentmods.dev/skills/scottstts/threejs-awesome-graphics-agent-skills/threejs-raymarched-space-effects"><img src="https://agentmods.dev/badge/skills/scottstts/threejs-awesome-graphics-agent-skills/threejs-raymarched-space-effects/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/scottstts/threejs-awesome-graphics-agent-skills/threejs-raymarched-space-effects"><img src="https://agentmods.dev/badge/skills/scottstts/threejs-awesome-graphics-agent-skills/threejs-raymarched-space-effects.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00077 | $0.00939 |
| Opus 5 | $0.00039 | $0.00469 |
| Sonnet 5 | $0.00015 | $0.00188 |
| Haiku 4.5 | $0.00008 | $0.00094 |
Grade A, and why
threejs-raymarched-space-effects 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 11d 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Raymarched Space Effects
Treat these effects as numerical renderers with explicit integration state. The visual character depends on coordinate choice, step policy, and how rays interact with emissive structures.
This skill contains exemplary examples and assets beyond descriptive guidance, they're worth studying, referencing, or even copying. Use them sufficiently when relevant and do NOT blindly skip them.
Workflow
- Define the effect-space transform and camera ray.
- Choose a physical, physically inspired, or purely artistic bending model.
- Bound the integration domain.
- Track ray position, direction, throughput, and accumulated radiance.
- Detect crossings with disks, shells, throats, or event boundaries.
- Sample the background only after integration terminates, filtered by the ray-bundle footprint rather than point sampled.
- Add diagnostics for trajectory, step count, and termination reason.
Read references/curved-ray-integrators.md for the two wormhole throat models, observer transport through a throat, the artistic curved-ray accretion integrator, disk composition, and implementation defects.
Read references/lensed-celestial-spheres.md for footprint-filtered skies, flux-conserving star point spreads, dust extinction, bright sources under display clipping, progressive accumulation, and the bloom kernel a compact source needs.
Read the curved-ray accretion volume for the inverse-square steering loop, thin disk density, front-to-back accumulation, deterministic star environment, and integrator diagnostics.
Read the Schwarzschild geodesic black-hole effect for RK2 null-geodesic integration, interpolated equatorial disk crossings, Doppler/redshift disk emission, lensed procedural deep field, and HDR bloom composition.
Read the traversable wormhole transit for adaptive RK4 integration of the reduced geodesic system, an observer frame parallel-transported through the throat, exterior-region selection, progressive Halton accumulation, and the 13-tap bloom pyramid; its celestial spheres are a standalone GLSL chunk for the footprint-filtered galactic sky, resolved star layers, and the analytic ringed planet a lensed ray lands on.
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.
- agents/openai.yaml 332 B
- assets/curved-ray-accretion-volume/noise_deep.png 959 KB
- examples/curved-ray-accretion-volume/curved-ray-effect.js 9.8 KB runs code
- examples/schwarzschild-geodesic-black-hole/geodesic-black-hole-effect.js 17 KB runs code
- examples/traversable-wormhole-transit/celestial-spheres.js 19 KB runs code
- examples/traversable-wormhole-transit/wormhole-effect.js 26 KB runs code
- references/curved-ray-integrators.md 12 KB
- references/lensed-celestial-spheres.md 14 KB
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.
- 11d ago First seen · 85 lines · 77 tokens per session scan A 5a9993119494
threejs-raymarched-space-effects is a skill published in the GitHub repository scottstts/Threejs-Awesome-Graphics-Agent-Skills (800 stars, last pushed today), licensed MIT. It adds 77 tokens to every session and 939 once invoked, about $0.0004 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 2026-08-30.
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