d3-force-simulation

A D3.js guide for placing bubbles on an interactive chart using simulated physical forces. It covers grouping bubbles into clusters and keeping them from overlapping.

In plain words
What is it for?
Use it to build bubble charts with clustered groups, collision detection, and physics-based movement in JavaScript.
Why use it?
It removes the need to calculate every bubble position manually. The simulation adjusts positions as the chart changes.

Skill for Claude CodeCodex

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.

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

Made for: Claude Code, Codex.

Per session 26 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,221 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00026 $0.01221
Opus 5 $0.00013 $0.00611
Sonnet 5 $0.00005 $0.00244
Haiku 4.5 $0.00003 $0.00122

Measured 3d ago against content hash 060ecc19e3cb, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

d3-force-simulation 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-claude-haiku-4-5/stock-data-visualization/d3-force-simulation/SKILL.md · 178 lines

How it starts

The opening of the file, as written. The whole thing — 178 lines — stays where its author put it; the contents beside it link to each section on GitHub.

D3.js Force Simulation

Overview

Force simulation in D3 uses physics-based algorithms to position nodes (bubbles) based on forces. Perfect for creating bubble charts with natural-looking clusters.

Core Concepts

1. Creating a Simulation

const simulation = d3.forceSimulation(nodes)
  .force("name", forceFunction)
  .on("tick", updatePositions);

// Start simulation (runs in background)
simulation.alpha(1);  // reset energy

2. Common Forces

forceX / forceY

Position bubbles toward target x/y coordinates (creates clustering):

// Group by sector - each sector gets target x position
const sectors = Array.from(new Set(data.map(d => d.sector)));
const sectorX = d3.scalePoint()
  .domain(sectors)
  .range([0, width]);

const simulation = d3.forceSimulation(nodes)
  .force("x", d3.forceX()
    .x(d => sectorX(d.sector))  // each sector pulled to its x position
    .strength(0.05)              // weak force (allows spreading)
  )
  .force("y", d3.forceY()
    .y(height / 2)               // all centered vertically
    .strength(0.03)
  );
forceCollide

Prevents bubbles from overlapping:

.force("collide", d3.forceCollide()
  .radius(d => radiusScale(d.marketCap) + 2)  // add padding
  .strength(0.5)  // collision strength (0-1)
)
forceManyBody

Repulsive or attractive force between all nodes:

.force("charge", d3.forceManyBody()
  .strength(-50)  // negative = repulsion, positive = attraction
)

3. Tick Events

Update positions on each simulation frame:

simulation.on("tick", () => {
  // Update circles
  circles
    .attr("cx", d => d.x)
    .attr("cy", d => d.y);

  // Update text
  labels
    .attr("x", d => d.x)
    .attr("y", d => d.y);
});

4. Preventing Bubbles from Moving Off-Screen

Fix nodes to boundaries:

simulation.on("tick", () => {
  nodes.forEach(d => {
    // Clamp positions within bounds
    d.x = Math.max(d.radius, Math.min(width - d.radius, d.x));
    d.y = Math.max(d.radius, Math.min(height - d.radius, d.y));
  });

  // Update positions
  circles.attr("cx", d => d.x).attr("cy", d => d.y);
});

Read the full file on GitHub · 178 lines

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 · 178 lines · 26 tokens per session scan A 060ecc19e3cb

Subscribe to this mod's changes

d3-force-simulation is a skill published in the GitHub repository cxcscmu/SkillLearnBench (82 stars, last pushed 1mo ago), licensed MIT. It adds 26 tokens to every session and 1,221 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 2026-08-30.

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