skills/d3-visualization/SKILL.md
Guides creation of custom, interactive data visualizations with D3.js — bar/line/scatter charts, network diagrams, geographic maps, hierarchies, and real-time data updates with zoom/pan/brush interactions and animated transitions. Use when chart libraries (Highcharts, Chart.js) lack the customization needed and you require low-level control over data-driven DOM manipulation, scales, shapes, and layouts. Invoke when user mentions D3, d3.js, custom visualization, force-directed graph, or data-driven SVG.
npx skillsauth add lyndonkl/claude d3-visualizationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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D3 provides low-level building blocks for data-driven DOM manipulation, visual encoding, layout algorithms, and interactions — enabling bespoke visualizations that chart libraries cannot provide.
Use D3 when: Chart libraries lack your specific design, you need full customization, you're building network graphs/hierarchies/maps, or animating data changes smoothly.
Prefer simpler tools when: Simple bar/line charts suffice (Chart.js, Highcharts), you need 3D (Three.js, WebGL), or datasets exceed 10K points without aggregation.
Core concepts: Data Joins (bind arrays to DOM elements), Scales (data values to visual values), Shapes (SVG path generation), Layouts (position calculation for networks/trees/maps), Transitions (animated state changes), Interactions (zoom/pan/drag/brush).
Choose a workflow based on your current task:
Use when: Building bar, line, or scatter charts from scratch
Time: 1-2 hours
Copy this checklist and track your progress:
Basic Chart Progress:
- [ ] Step 1: Set up SVG container with margins
- [ ] Step 2: Load and parse data
- [ ] Step 3: Create scales (x, y)
- [ ] Step 4: Generate axes
- [ ] Step 5: Bind data and create visual elements
- [ ] Step 6: Style and add interactivity
Step 1: Set up SVG container with margins
Create SVG element with proper dimensions. Define margins for axes: {top: 20, right: 20, bottom: 30, left: 40}. Calculate inner width/height: width - margin.left - margin.right. See Getting Started.
Step 2: Load and parse data
Use d3.csv('data.csv') for external files or define data array directly. Parse dates with d3.timeParse('%Y-%m-%d') for time series. Convert strings to numbers for CSV data using conversion function. See Getting Started.
Step 3: Create scales
Choose scale types based on data: scaleBand (categorical), scaleTime (temporal), scaleLinear (quantitative). Set domains from data using d3.extent(), d3.max(), or manual ranges. Set ranges from SVG dimensions. See Scales & Axes.
Step 4: Generate axes
Create axis generators: d3.axisBottom(xScale), d3.axisLeft(yScale). Append g elements positioned with transforms. Call axis generators: .call(axis). Customize ticks with .ticks(), .tickFormat(). See Scales & Axes.
Step 5: Bind data and create visual elements
Use data join pattern: svg.selectAll(type).data(array).join(type). Set attributes using scales and accessor functions: .attr('x', d => xScale(d.category)). For line charts, use d3.line() generator. For scatter plots, create circles with cx, cy, r attributes. See Selections & Data Joins and Shapes & Layouts.
Step 6: Style and add interactivity
Apply colors: .attr('fill', ...), .attr('stroke', ...). Add hover effects: .on('mouseover', ...) with tooltip. Add click handlers for drill-down. Apply transitions for initial animation (optional). See Transitions & Interactions and Common Patterns.
Use when: Refreshing charts with new data (real-time, filters, user interactions)
Time: 30 minutes - 1 hour
Copy this checklist:
Update Progress:
- [ ] Step 1: Encapsulate visualization in update function
- [ ] Step 2: Update scale domains if needed
- [ ] Step 3: Re-bind data with key function
- [ ] Step 4: Add transitions to join
- [ ] Step 5: Update attributes with new values
- [ ] Step 6: Trigger update on data change
Step 1: Encapsulate visualization in update function
Wrap steps 3-5 from basic chart workflow in function update(newData) { ... }. This makes visualization reusable for any dataset. See Workflows.
Step 2: Update scale domains
Recalculate domains when data range changes: yScale.domain([0, d3.max(newData, d => d.value)]). Update axes with transition: svg.select('.y-axis').transition().duration(500).call(yAxis). See Selections & Data Joins.
Step 3: Re-bind data with key function
Use key function for object constancy: .data(newData, d => d.id). Ensures elements track data items, not array positions. Critical for correct transitions. See Selections & Data Joins.
Step 4: Add transitions to join
Insert .transition().duration(500) before attribute updates. Specify easing with .ease(d3.easeCubicOut). For custom enter/exit effects, use enter/exit functions in .join(). See Transitions & Interactions.
Step 5: Update attributes with new values
Set positions/sizes using updated scales: .attr('y', d => yScale(d.value)), .attr('height', d => height - yScale(d.value)). Transitions animate from old to new values. See Common Patterns.
Step 6: Trigger update on data change
Call update(newData) when data changes: button clicks, timers (setInterval), WebSocket messages, API responses. For real-time, use sliding window to limit data points. See Workflows.
Use when: Building network graphs, hierarchies, or geographic maps
Time: 2-4 hours
Copy this checklist:
Advanced Layout Progress:
- [ ] Step 1: Choose appropriate layout type
- [ ] Step 2: Prepare and structure data
- [ ] Step 3: Create and configure layout
- [ ] Step 4: Apply layout to data
- [ ] Step 5: Bind computed properties to elements
- [ ] Step 6: Add interactions (drag, zoom)
Step 1: Choose appropriate layout type
Force Simulation: Network diagrams, organic clustering. Hierarchies: Tree, cluster (node-link), treemap, pack, partition (space-filling). Geographic: Maps with projections. Chord: Flow diagrams. See Advanced Layouts for decision guidance.
Step 2: Prepare and structure data
Force: nodes = [{id, group}], links = [{source, target}]. Hierarchy: Nested objects with children arrays, convert with d3.hierarchy(data). Geographic: GeoJSON features. See Advanced Layouts.
Step 3: Create and configure layout
Force: d3.forceSimulation(nodes).force('link', d3.forceLink(links)).force('charge', d3.forceManyBody()). Hierarchy: d3.treemap().size([width, height]). Geographic: d3.geoMercator().fitExtent([[0,0], [width,height]], geojson). See Advanced Layouts for each layout type.
Step 4: Apply layout to data
Force: Simulation runs automatically, updates node positions each tick. Hierarchy: Call layout on root: treemap(root). Geographic: No application needed, projection used in path generator. See Advanced Layouts.
Step 5: Bind computed properties to elements
Force: Update cx, cy in tick handler: node.attr('cx', d => d.x). Hierarchy: Use d.x0, d.x1, d.y0, d.y1 for rectangles. Geographic: Use path(feature) for d attribute. See Workflows for layout-specific examples.
Step 6: Add interactions
Drag for force networks: node.call(d3.drag().on('drag', dragHandler)). Zoom for maps/large networks: svg.call(d3.zoom().on('zoom', zoomHandler)). Click for hierarchy drill-down. See Transitions & Interactions.
What would you like to do?
I'm new to D3 - Setup environment, understand prerequisites, create first visualization
Build a basic chart - Bar, line, or scatter plot step-by-step
Transform data with scales - Map data values to visual properties (positions, colors, sizes)
Bind data to elements - Connect arrays to DOM elements, handle dynamic updates
Create network/hierarchy/map - Force-directed graphs, treemaps, geographic visualizations
Add animations - Smooth transitions between chart states
Add interactivity - Zoom, pan, drag, brush selection, tooltips
Update chart with new data - Handle real-time data, filters, user interactions
Get code templates - Copy-paste-modify templates for common patterns
Understand D3 concepts - Deep dive into data joins, scales, generators, layouts
// 1. Select container
const svg = d3.select('svg');
// 2. Bind data
svg.selectAll('circle')
.data(dataArray)
.join('circle') // Create/update/remove elements automatically
.attr('cx', d => d.x) // Use accessor functions (d = datum, i = index)
.attr('cy', d => d.y)
.attr('r', 5);
// For quantitative data
const xScale = d3.scaleLinear()
.domain([0, 100]) // Data range
.range([0, 500]); // Pixel range
// For categorical data
const xScale = d3.scaleBand()
.domain(['A', 'B', 'C'])
.range([0, 500])
.padding(0.1);
// For temporal data
const xScale = d3.scaleTime()
.domain([new Date(2020, 0, 1), new Date(2020, 11, 31)])
.range([0, 500]);
// Line chart
const line = d3.line()
.x(d => xScale(d.date))
.y(d => yScale(d.value));
svg.append('path')
.datum(data) // Use .datum() for single data item
.attr('d', line) // Line generator creates path data
.attr('fill', 'none')
.attr('stroke', 'steelblue');
// Add transition before attribute updates
svg.selectAll('rect')
.data(newData)
.join('rect')
.transition() // Everything after this is animated
.duration(500) // Milliseconds
.attr('height', d => yScale(d.value));
| Data Type | Task | Scale |
|-----------|------|-------|
| Quantitative (linear) | Position, size | scaleLinear() |
| Quantitative (exponential) | Compress range | scaleLog(), scalePow() |
| Quantitative → Circle area | Size circles | scaleSqrt() |
| Categorical | Bars, groups | scaleBand(), scalePoint() |
| Categorical → Colors | Color encoding | scaleOrdinal() |
| Temporal | Time series | scaleTime() |
| Quantitative → Colors | Heatmaps | scaleSequential() |
// Specific functions
import { select, selectAll } from 'd3-selection';
import { scaleLinear, scaleBand } from 'd3-scale';
import { line, area } from 'd3-shape';
// Entire D3 namespace
import * as d3 from 'd3';
testing
Cluster a conference's event records into a small set of coarse themes with finer sub-clusters, an explicit outlier bucket, and soft (multi-membership) affinities — using the hybrid embed-then-label pipeline (embed abstracts, reduce, density-cluster, then LLM-label the clusters) when embedding libraries are available, and an LLM-reasoned hierarchical fallback when they are not. Embeddings do the grouping; the LLM only names the groups. Conference-agnostic. Use when turning structured event records into a navigable theme map for preference elicitation and scheduling, when you need 6-8 reasonable themes rather than 20 muddy ones, or when overlapping talks must belong to more than one theme. Trigger keywords - theme clustering, cluster talks, embed then label, soft membership, outlier talks, conference themes, topic map.
development
Build a personal conference schedule as a constraint-optimization problem — hard constraints (no time overlap, room-to-room travel time, capacity/registration, the attendee's own must-attends and blackouts) plus a user-owned weighted objective trading interest against breadth, pacing (maximize contiguous free time), and serendipity. Surfaces unbreakable conflicts (two high-value overlapping talks the model cannot rank) as decisions for the human rather than silently picking, and reports what each choice traded away. Conference-agnostic. Use to turn a preference profile plus a theme map into a day-by-day plan, to resolve overlapping sessions, or to balance a packed vs paced schedule. Trigger keywords - schedule optimization, conference schedule, constraint optimization, overlapping talks, contiguous free time, conflict surfacing, packed vs paced.
development
Parse a heterogeneous conference program (markdown, HTML, PDF-derived text, or JSON) into normalized event records with per-field confidence scores and independent classification axes (topic, depth, format, prerequisites, recorded, capacity). Detects the program's format before extracting, treats every inferred field as uncertain (present vs inferred vs missing), and flags thin or missing abstracts so downstream enrichment can target them. Conference-agnostic. Use when ingesting a conference or event schedule into a structured store, normalizing a talk/session list, or extracting per-session metadata with calibrated confidence. Trigger keywords - program ingestion, parse schedule, session extraction, event records, conference program, talk metadata, per-field confidence.
development
Build a personalized preference profile from a small number of well-chosen, cluster-grounded questions instead of a long survey. Represents the person's interests as an uncertainty region over the theme map, picks the single highest-information-gain choice-based question (contrasting real talks from different clusters), balances exploiting known interests against exploring uncertain ones, deliberately injects outlier probes to fight selection bias, and stops as soon as the schedule would be stable. Also elicits the user-owned objective weights and hard constraints. Interactive — runs where it can actually ask the person. Conference-agnostic. Use to turn a theme map into a preference profile, to decide what to ask a conference attendee, or to elicit scheduling priorities. Trigger keywords - preference elicitation, ask few questions, information gain, choice-based questions, selection bias probe, objective weights, attendee preferences.