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 cxcscmu/SkillLearnBench --skill d3-force-clusteringgit clone --depth 1 https://github.com/cxcscmu/SkillLearnBenchWrote 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/cxcscmu/skilllearnbench/d3-force-clustering)<a href="https://agentmods.dev/skills/cxcscmu/skilllearnbench/d3-force-clustering"><img src="https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/d3-force-clustering.svg" alt="Measured on agentmods" 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 | $0.00022 | $0.00413 |
| Opus 5 | $0.00011 | $0.00206 |
| Sonnet 5 | $0.00004 | $0.00083 |
| Haiku 4.5 | $0.00002 | $0.00041 |
Grade A, and why
d3-force-clustering 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 3d 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.
What it actually says
D3.js Force Simulation for Clustered Bubble Charts
D3's force simulation is essential for creating bubble charts where elements are grouped by categories (clusters) and prevented from overlapping.
Key Forces
- forceSimulation: The engine that updates positions.
- forceX / forceY: Attracts nodes to specific coordinates. Use this to create clusters by mapping categories to center points.
- forceCollide: Prevents nodes from overlapping by specifying a radius.
- forceCenter: Keeps the entire group of nodes centered in the SVG.
Usage Pattern
const simulation = d3.forceSimulation(data)
.force("x", d3.forceX(d => clusterCenters[d.sector].x).strength(0.1))
.force("y", d3.forceY(d => clusterCenters[d.sector].y).strength(0.1))
.force("collide", d3.forceCollide(d => radiusScale(d.value) + 2))
.force("center", d3.forceCenter(width / 2, height / 2))
.on("tick", ticked);
function ticked() {
nodes
.attr("cx", d => d.x)
.attr("cy", d => d.y);
labels
.attr("x", d => d.x)
.attr("y", d => d.y);
}
Clustering Strategy
To group nodes by a "Sector" attribute:
- Define a set of center points for each sector (e.g., arranged in a grid or circle).
- Apply
forceXandforceYtargeting those centers. - Use a moderate
strength(e.g., 0.1) to allow collision force to resolve overlaps while maintaining the cluster shape.
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.
- 3d ago First seen · 43 lines · 22 tokens per session scan A a84d63e01162
d3-force-clustering is a skill published in the GitHub repository cxcscmu/SkillLearnBench (83 stars, last pushed 1mo ago), licensed MIT. It adds 22 tokens to every session and 413 once invoked, about $0.0001 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 2026-09-03.
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