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 Bbeierle12/Skill-MCP-Claude --skill particles-gpugit clone --depth 1 https://github.com/Bbeierle12/Skill-MCP-ClaudeWrote 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/bbeierle12/skill-mcp-claude/particles-gpu)<a href="https://agentmods.dev/skills/bbeierle12/skill-mcp-claude/particles-gpu"><img src="https://agentmods.dev/badge/skills/bbeierle12/skill-mcp-claude/particles-gpu/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/bbeierle12/skill-mcp-claude/particles-gpu"><img src="https://agentmods.dev/badge/skills/bbeierle12/skill-mcp-claude/particles-gpu.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 | $0.00048 | $0.03932 |
| Opus 5 | $0.00024 | $0.01966 |
| Sonnet 5 | $0.00010 | $0.00786 |
| Haiku 4.5 | $0.00005 | $0.00393 |
Grade A, and why
particles-gpu 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 9d 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 — 524 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GPU Particles
Render massive particle counts (10k-1M+) efficiently using GPU instancing and custom shaders.
Quick Start
import { useRef, useMemo } from 'react';
import { useFrame } from '@react-three/fiber';
import * as THREE from 'three';
function Particles({ count = 10000 }) {
const points = useRef<THREE.Points>(null!);
const positions = useMemo(() => {
const pos = new Float32Array(count * 3);
for (let i = 0; i < count; i++) {
pos[i * 3] = (Math.random() - 0.5) * 10;
pos[i * 3 + 1] = (Math.random() - 0.5) * 10;
pos[i * 3 + 2] = (Math.random() - 0.5) * 10;
}
return pos;
}, [count]);
return (
<points ref={points}>
<bufferGeometry>
<bufferAttribute
attach="attributes-position"
count={count}
array={positions}
itemSize={3}
/>
</bufferGeometry>
<pointsMaterial size={0.05} color="#ffffff" />
</points>
);
}
Rendering Approaches
| Approach | Particle Count | Complexity | Use Case |
|---|---|---|---|
| Points | 10k - 500k | Low | Simple particles, stars |
| Instanced Mesh | 1k - 100k | Medium | 3D geometry particles |
| Custom Shader | 100k - 10M | High | Maximum control |
Points Geometry
Simplest approach—each particle is a screen-facing point sprite.
Basic Points
function BasicPoints({ count = 5000 }) {
const positions = useMemo(() => {
const pos = new Float32Array(count * 3);
for (let i = 0; i < count; i++) {
const theta = Math.random() * Math.PI * 2;
const phi = Math.acos(2 * Math.random() - 1);
const r = Math.cbrt(Math.random()) * 5;
pos[i * 3] = r * Math.sin(phi) * Math.cos(theta);
pos[i * 3 + 1] = r * Math.sin(phi) * Math.sin(theta);
pos[i * 3 + 2] = r * Math.cos(phi);
}
return pos;
}, [count]);
return (
<points>
<bufferGeometry>
<bufferAttribute
attach="attributes-position"
count={count}
array={positions}
itemSize={3}
/>
</bufferGeometry>
<pointsMaterial
size={0.1}
sizeAttenuation={true}
transparent={true}
opacity={0.8}
depthWrite={false}
blending={THREE.AdditiveBlending}
/>
</points>
);
}
What ships with it
2 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.
- 9d ago First seen · 524 lines · 48 tokens per session scan A 7c0db2ff1e62
particles-gpu is a skill published in the GitHub repository Bbeierle12/Skill-MCP-Claude (8 stars, last pushed today), licensed MIT. It adds 48 tokens to every session and 3,932 once invoked, about $0.0002 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.
Other skills, from other repositories
flask-werkzeug-attack
Exploit Flask/Werkzeug debugger exposure for traceback and SECRET leaks.
flask-expert
Expert-level Flask web development, REST APIs, extensions, and production deployment. Use when the user mentions Python, web framework, REST APIs, or Jinja2, or when the task involves Flask Fundamentals or Flask Extensions.
fast-dash
Build a Fast Dash web app from a Python function. Use when the user wants to turn a function into an interactive app, add a UI to an existing function, or build a dashboard / form / wizard. Fast Dash infers UI components from type hints, so a well-typed function becomes an app with one decorator.
flask
Flask - Lightweight Python web framework for microservices, REST APIs, and flexible web applications with extensive extension ecosystem.
flask
Operational skill for Flask: app factories, blueprints, request context, Jinja/JSON APIs, extensions, and pytest testing patterns.
flask
Flask - Lightweight Python web framework for microservices, REST APIs, and flexible web applications with extensive extension ecosystem.