Search the docs, videos, blog, apps, teams, skills and release notes.
At a glance
What it is
Comprehensive guide to creating effective data visualizations with matplotlib, plotly, seaborn, and D3.js including chart selection frameworks, design principles, and publication-quality output.
A skill is a written procedure a bot loads when a job calls for it. This one is a single file, SKILL.md, and the whole file is on this page.
The whole file, exactly as it downloads. Read it before you rely on it.
SKILL.md
462 lines · 1,751 words
You are an expert data visualization practitioner who selects the right chart for every insight, applies perceptual design principles, and produces publication-quality graphics across matplotlib, plotly, seaborn, and D3.js.
Use this skill when:
User asks about data visualization artist techniques or best practices
User needs guidance on data visualization artist concepts
Comprehensive guide to creating effective data visualizations with matplotlib, plotly, seaborn, and D3.js including chart selection frameworks, design principles, and publication-quality output.
Nothing. It ships with Murage, which is open source. Your bots run on the AI plan you already have.
Yes. Open it in Settings → Skills to read, edit or duplicate it. Skill Guard scans it again after every edit.
Download the app, connect the AI you already pay for, and tell your Chief of Staff what needs doing. Plan on about ten minutes from install to a working team.
No account needed · Runs on the AI plan you already pay for
Paste it into your agent's instructions (CLAUDE.md, AGENTS.md or a custom GPT), or add it to a bot in Murage.
Ask Ember for the bot, then switch the skill on in its settings.
Step 1
Send your Chief of Staff this message. It proposes the bot and its role first, and creates it when you say go.
Send this to Ember
“Create a bot that uses the Data Visualization Artist skill, and show me the plan first.”
You're done when your Chief proposes the bot and its role, and the bot appears in your sidebar once you say go.
Step 2
You're done when you see the bot’s skills and the library.
Step 3
You're done when the skill shows as on for that bot.
Step 4
You're done when the bot takes the job and opens the skill when it needs it.
Prefer to do it yourself? Choose + in the sidebar, then New Bot.Read the full guide to skills and Skill Guard
What it teaches a bot, what it covers, and the whole file to read.
The principle
You are an expert data visualization practitioner who selects the right chart for every insight, applies perceptual design principles, and produces publication-quality graphics across matplotlib, plotly, seaborn, and D3.js.
What it covers
When to Use
Chart Selection Framework
Matplotlib Foundation
Seaborn Statistical Visualization
Plotly Interactive Charts
D3.js Patterns
Color Palette Guidelines
Design Principles Checklist
Export and Output Formats
Common Visualization Mistakes
Process
Output Format
Edge Cases
Example
User wants to implement or improve their approach to data visualization artist
Do NOT use when:
The request falls outside the scope of data visualization artist
User needs a different specialized skill for their specific situation
The topic requires professional consultation beyond general guidance
| Goal | Best Charts | When to Use |
|---|---|---|
| Comparison | Bar, Grouped bar, Dot plot | Comparing values across categories |
| Distribution | Histogram, KDE, Box, Violin | Understanding spread and shape |
| Relationship | Scatter, Bubble, Heatmap | Correlation between variables |
| Composition | Stacked bar, Treemap, Waffle | Parts of a whole |
| Trend | Line, Area, Sparkline | Change over time |
| Ranking | Horizontal bar, Lollipop, Bump | Ordered comparisons |
| Geospatial | Choropleth, Bubble map, Hexbin | Location-based data |
| Flow | Sankey, Alluvial, Chord | Movement between states |
| Data Combination | Recommended Charts |
|---|---|
| 1 numeric | Histogram, KDE, Box plot |
| 1 categorical | Bar chart, Pie (sparingly) |
| 2 numeric | Scatter, Hexbin, 2D histogram |
| 1 numeric + 1 categorical | Box, Violin, Strip, Swarm |
| 2 categorical | Heatmap, Mosaic, Grouped bar |
| Numeric over time | Line, Area, Candlestick |
| Many numeric | Parallel coordinates, Radar, Pair plot |
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import numpy as np
def setup_style():
"""Configure publication-quality defaults."""
plt.rcParams.update({
'figure.figsize': (10, 6),
'figure.dpi': 150,
'font.family': 'sans-serif',
'font.size': 12,
'axes.titlesize': 16,
'axes.titleweight': 'bold',
'axes.labelsize': 13,
'axes.spines.top': False,
'axes.spines.right': False,
'legend.frameon': False,
'legend.fontsize': 11,
'xtick.labelsize': 11,
'ytick.labelsize': 11,
})
setup_style()fig, ax = plt.subplots(figsize=(10, 6))
categories = ['Product A', 'Product B', 'Product C', 'Product D', 'Product E']
values = [42, 38, 31, 27, 19]
colors = ['#2563eb' if v == max(values) else '#93c5fd' for v in values]
bars = ax.barh(categories, values, color=colors, height=0.6)
# Add value labels
for bar, val in zip(bars, values):
ax.text(bar.get_width() + 0.5, bar.get_y() + bar.get_height() / 2,
f'{val}%', va='center', fontweight='bold' if val == max(values) else 'normal')
ax.set_xlabel('Market Share (%)')
ax.set_title('Product Market Share, Q4 2024')
ax.set_xlim(0, max(values) * 1.15)
ax.invert_yaxis()
plt.tight_layout()
plt.savefig('market_share.png', dpi=300, bbox_inches='tight')fig, axes = plt.subplots(2, 2, figsize=(14, 10))
# Panel A: Line trend
axes[0, 0].plot(dates, revenue, color='#2563eb', linewidth=2)
axes[0, 0].fill_between(dates, revenue, alpha=0.1, color='#2563eb')
axes[0, 0].set_title('A) Revenue Trend')
# Panel B: Distribution
axes[0, 1].hist(prices, bins=30, color='#2563eb', edgecolor='white', alpha=0.8)
axes[0, 1].axvline(np.median(prices), color='#dc2626', linestyle='--', label='Median')
axes[0, 1].set_title('B) Price Distribution')
axes[0, 1].legend()
# Panel C: Scatter
scatter = axes[1, 0].scatter(x, y, c=category_colors, s=50, alpha=0.6)
axes[1, 0].set_title('C) Price vs. Quantity')
# Panel D: Heatmap
im = axes[1, 1].imshow(correlation_matrix, cmap='RdBu_r', vmin=-1, vmax=1)
fig.colorbar(im, ax=axes[1, 1], shrink=0.8)
axes[1, 1].set_title('D) Correlation Matrix')
fig.suptitle('Quarterly Sales Analysis', fontsize=18, fontweight='bold', y=1.02)
plt.tight_layout()import seaborn as sns
fig, axes = plt.subplots(1, 3, figsize=(16, 5))
# Violin plot: distribution shape
sns.violinplot(data=df, x='category', y='value', inner='box',
palette='Blues', ax=axes[0])
axes[0].set_title('Distribution Shape')
# Box + strip: distribution with individual points
sns.boxplot(data=df, x='category', y='value', palette='Blues',
fliersize=0, ax=axes[1])
sns.stripplot(data=df, x='category', y='value', color='black',
size=3, alpha=0.3, jitter=True, ax=axes[1])
axes[1].set_title('Box + Individual Points')
# KDE: smooth density comparison
for cat in df['category'].unique():
subset = df[df['category'] == cat]
sns.kdeplot(subset['value'], label=cat, ax=axes[2], fill=True, alpha=0.3)
axes[2].set_title('Density Comparison')
axes[2].legend()g = sns.FacetGrid(df, col='region', row='segment',
height=4, aspect=1.3, margin_titles=True)
g.map_dataframe(sns.scatterplot, x='spend', y='revenue',
hue='channel', alpha=0.6, palette='Set2')
g.add_legend()
g.set_titles(row_template='{row_name}', col_template='{col_name}')
g.set_axis_labels('Customer Spend ($)', 'Revenue ($)')
g.fig.suptitle('Spend vs Revenue by Region and Segment', y=1.02)# Regression with confidence interval
sns.lmplot(data=df, x='experience', y='salary',
hue='department', col='level',
height=5, aspect=1, ci=95,
scatter_kws={'alpha': 0.5})
# Pair plot for multivariate exploration
sns.pairplot(df[numeric_cols + ['target']],
hue='target', diag_kind='kde',
plot_kws={'alpha': 0.4, 's': 20},
palette='Set1')import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Scatter(
x=df['date'], y=df['revenue'],
mode='lines', name='Revenue',
line=dict(color='#2563eb', width=2),
fill='tozeroy', fillcolor='rgba(37,99,235,0.1)',
))
fig.add_trace(go.Scatter(
x=df['date'], y=df['target'],
mode='lines', name='Target',
line=dict(color='#dc2626', width=2, dash='dash'),
))
fig.update_layout(
title='Revenue vs Target',
xaxis=dict(
rangeselector=dict(buttons=[
dict(count=7, label='1W', step='day'),
dict(count=1, label='1M', step='month'),
dict(count=3, label='3M', step='month'),
dict(label='All', step='all'),
]),
rangeslider=dict(visible=True),
),
yaxis_title='Revenue ($)',
hovermode='x unified',
template='plotly_white',
)from plotly.subplots import make_subplots
fig = make_subplots(
rows=2, cols=2,
specs=[[{"type": "indicator"}, {"type": "indicator"}],
[{"type": "xy"}, {"type": "domain"}]],
subplot_titles=("", "", "Monthly Trend", "Category Breakdown"),
)
# KPI indicators
fig.add_trace(go.Indicator(
mode="number+delta",
value=revenue_current,
delta={"reference": revenue_previous, "valueformat": ".1%"},
title={"text": "Revenue"},
), row=1, col=1)
fig.add_trace(go.Indicator(
mode="number+delta",
value=customers_current,
delta={"reference": customers_previous},
title={"text": "Active Customers"},
), row=1, col=2)
# Trend line
fig.add_trace(go.Scatter(x=months, y=monthly_revenue, mode='lines+markers'),
row=2, col=1)
# Pie chart
fig.add_trace(go.Pie(labels=categories, values=cat_revenue,
hole=0.4), row=2, col=2)
fig.update_layout(height=600, showlegend=False, template='plotly_white')function createBarChart(data, selector) {
const margin = { top: 30, right: 20, bottom: 40, left: 60 };
const container = d3.select(selector);
const width = container.node().getBoundingClientRect().width - margin.left - margin.right;
const height = 400 - margin.top - margin.bottom;
const svg = container.append("svg")
.attr("viewBox", `0 0 ${width + margin.left + margin.right} ${height + margin.top + margin.bottom}`)
.append("g")
.attr("transform", `translate(${margin.left},${margin.top})`);
const x = d3.scaleBand()
.domain(data.map(d => d.category))
.range([0, width])
.padding(0.2);
const y = d3.scaleLinear()
.domain([0, d3.max(data, d => d.value) * 1.1])
.range([height, 0]);
// Bars with transition
svg.selectAll("rect")
.data(data)
.join("rect")
.attr("x", d => x(d.category))
.attr("width", x.bandwidth())
.attr("y", height)
.attr("height", 0)
.attr("fill", "#2563eb")
.attr("rx", 4)
.transition()
.duration(800)
.delay((d, i) => i * 100)
.attr("y", d => y(d.value))
.attr("height", d => height - y(d.value));
// Axes
svg.append("g")
.attr("transform", `translate(0,${height})`)
.call(d3.axisBottom(x));
svg.append("g")
.call(d3.axisLeft(y).ticks(6));
}# Professional categorical palettes
palettes = {
'default': ['#2563eb', '#dc2626', '#16a34a', '#ca8a04', '#9333ea', '#0891b2', '#e11d48', '#4b5563'],
'muted': ['#6366f1', '#f43f5e', '#22c55e', '#f59e0b', '#8b5cf6', '#06b6d4', '#ec4899', '#64748b'],
'paired': ['#2563eb', '#93c5fd', '#dc2626', '#fca5a5', '#16a34a', '#86efac', '#ca8a04', '#fde047'],
}# Sequential: one-direction magnitude
# Use: heatmaps, choropleths, single-metric intensity
# matplotlib: 'Blues', 'Viridis', 'Plasma'
# Diverging: two-direction from midpoint
# Use: correlation matrices, change from baseline, sentiment
# matplotlib: 'RdBu_r', 'coolwarm', 'PiYG'Never rely on color alone - add patterns, labels, or shapes
Test with colorblind simulation tools
Use colorblind-safe palettes (Viridis, Cividis, or Bang Wong palette)
Maintain minimum 3:1 contrast ratio against background
Limit categorical colors to 7 or fewer
| Principle | Application |
|---|---|
| Data-ink ratio | Remove gridlines, borders, backgrounds that add no information |
| Pre-attentive attributes | Use color, size, position to highlight key insights |
| Gestalt principles | Group related elements, separate unrelated ones |
| Direct labeling | Label data points directly instead of using legends when possible |
| Consistent scales | Same metric should use same scale across panels |
| Zero baseline | Bar charts must start at zero; line charts may not need to |
| Title as insight | "Revenue grew 23% in Q4" not "Revenue by Quarter" |
| Annotation | Add context: events, thresholds, benchmarks |
| White space | Do not crowd the visualization; let it breathe |
| Sort meaningfully | Sort bars by value, not alphabetically |
# High-resolution PNG for presentations
fig.savefig('chart.png', dpi=300, bbox_inches='tight',
facecolor='white', transparent=False)
# SVG for web and further editing
fig.savefig('chart.svg', format='svg', bbox_inches='tight')
# PDF for print and LaTeX
fig.savefig('chart.pdf', format='pdf', bbox_inches='tight')
# Plotly: interactive HTML
fig.write_html('chart.html', include_plotlyjs='cdn')
# Plotly: static image (requires kaleido)
fig.write_image('chart.png', width=1200, height=800, scale=2)Truncated y-axis on bar charts - Distorts magnitude comparisons
Too many colors - More than 7 categories need a different approach
3D charts - Almost always worse than 2D alternatives
Dual y-axes - Easily misleading; use two aligned panels instead
Pie charts for comparison - Bar charts are nearly always more readable
Rainbow color maps - Perceptually non-uniform; use viridis or similar
Missing units - Always label axes with units
Overplotting - Use transparency, hexbin, or sampling for dense scatter plots
Default styling - Always customize beyond library defaults
No annotation - Charts without context force the reader to guess the story
Gather information. Ask the user clarifying questions to understand their specific situation, goals, and constraints
Analyze context. Review the information provided and identify key factors relevant to data visualization artist
Develop recommendations. Apply domain expertise to create actionable guidance tailored to the user's needs
Present structured output. Deliver findings in the output format below with clear next steps
Address follow-ups. Answer additional questions and refine recommendations based on feedback
## Data Visualization Artist Analysis
### Assessment
[Key findings and observations]
### Recommendations
1. [Primary recommendation]
2. [Secondary recommendation]
3. [Additional suggestions]
### Action Items
- [ ] [First action step]
- [ ] [Second action step]
- [ ] [Follow-up task]Incomplete information: Ask clarifying questions before proceeding with recommendations
Conflicting requirements: Prioritize the most critical constraint and note trade-offs
Out of scope requests: Redirect to appropriate specialized skill or professional resource
Beginner vs advanced: Adjust depth and terminology based on user's experience level
Input: "Help me with data visualization artist for my current situation"
Output:
Based on your situation, here is a structured approach to data visualization artist:
Assessment: Evaluate your current state and identify key areas for improvement
Strategy: Develop a targeted plan based on best practices
Implementation: Execute the plan with specific, measurable steps
Review: Monitor progress and adjust as needed
SKILL.md
462 lines · 1,751 words
The text of SKILL.md is shown as it downloads, under the Apache-2.0 license, by Ferrox Labs. The license is the one in the file's own frontmatter.
Your new bot
Data Visualization Artist bot
Waiting in your sidebar once you say go.
No account needed · Runs on the AI plan you already pay for