d3-force-clustering

d3-force-clustering is a skill for Claude Code, Codex from cxcscmu/SkillLearnBench. It costs 22 tokens per session (413 once invoked), scanned A, original, MIT.

A D3.js technique for making bubble-chart items group around category centers without overlapping. D3.js is a JavaScript library for creating interactive web visualizations, and a force simulation adjusts item positions automatically.

In plain words
What is it for?
It helps create clustered bubble charts, place groups by category, prevent bubbles from colliding, and show labels.
Why use it?
It removes the need to calculate every bubble position manually while keeping related items together.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It helps create clustered bubble charts, place groups by category, prevent bubbles from colliding, and show labels.

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Install with agentmods
npx agentmods add skills/cxcscmu/skilllearnbench/d3-force-clustering
Install

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.

Any agent
npx skills add cxcscmu/SkillLearnBench --skill d3-force-clustering
Clone the repo
git clone --depth 1 https://github.com/cxcscmu/SkillLearnBench

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for d3-force-clustering

README.md
[![agentmods](https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/d3-force-clustering.svg)](https://agentmods.dev/skills/cxcscmu/skilllearnbench/d3-force-clustering)
Your own site
<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>
Per session 22 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 413 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 3d ago against content hash a84d63e01162, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

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.

skills/b1-one-shot-gemini-3-flash-preview/stock-data-visualization/d3-force-clustering/SKILL.md · 43 lines

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

  1. forceSimulation: The engine that updates positions.
  2. forceX / forceY: Attracts nodes to specific coordinates. Use this to create clusters by mapping categories to center points.
  3. forceCollide: Prevents nodes from overlapping by specifying a radius.
  4. 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:

  1. Define a set of center points for each sector (e.g., arranged in a grid or circle).
  2. Apply forceX and forceY targeting those centers.
  3. Use a moderate strength (e.g., 0.1) to allow collision force to resolve overlaps while maintaining the cluster shape.
Changes

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.

  1. 3d ago First seen · 43 lines · 22 tokens per session scan A a84d63e01162

Subscribe to this mod's changes

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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