$ ~/registry/skill/anthropics-data-skills-data-visualization

SKILL

data-visualization

Name: data-visualization
Author: anthropics

Create clear, professional Python data visualizations and choose suitable chart types.

Stars

★ 22,528

Source

GitHub

Updated

2026-07-11

// securityCaution

Prompt only, no code
Open source

Summary

The material indicates this is essentially an open-source prompt/documentation-style data visualization skill. It requires no secrets, declares no remote endpoints, and does not show autonomous code execution or data exfiltration, so overall risk is low. The main caveat is supply-chain confidence: it is open-source on GitHub, but community adoption is low and the license/maintenance status are unclear.

CredentialsLow risk

The material explicitly states that no keys or environment variables are required. No API keys, OAuth tokens, or other sensitive credentials are mentioned, so credential exposure and abuse risk appears low.

Network egressLow risk

The material states there are no remote endpoint hosts, and the content is limited to chart selection guidance and Python visualization examples. There is no indication of sending user data to external services or connecting to third-party endpoints.

Code executionLow risk

The system checks classify it as prompt-only. The README presents matplotlib/seaborn/plotly code patterns and design guidance, but does not indicate that the skill itself launches local processes, executes scripts, or requests additional system capabilities.

Data accessLow risk

The material does not declare access to local files, databases, cloud storage, or other resources. As a documentation/prompt-style skill, its data access scope appears limited to ordinary text guidance, with no signs of overbroad permissions.

Supply chainCaution

A positive factor is that the source is on GitHub and open-source, making code review possible. However, the repository shows 0 stars, no declared license, and unknown maintenance status, so while there is some transparency, supply-chain confidence still warrants caution.

Recommendations

If you plan to run the Python examples from the README, review the code first and use an isolated environment for dependencies.
Verify the repository license, recent commit history, and maintainer identity before using it in production.
If future versions add file access, networking, or automatic execution capabilities, re-audit the skill.

Audited by: gpt-5.4 · 2026-06-16

// install

Copy the install command and let the AI configure it · recommended for beginners

Please install the "data-visualization" skill from askskill:
1. Download https://raw.githubusercontent.com/anthropics/knowledge-work-plugins/main/data/skills/data-visualization/SKILL.md
2. Save it as ~/.claude/skills/data-visualization/SKILL.md
3. Reload skills and tell me it's ready

// download

Download SKILL.mdInstall manifest ↗

// examples

Choose charts for sales data

Prompt

I have a table with month, region, sales, and profit margin. First decide the best chart type for each field relationship, then generate 3 clear visualization options in Python using matplotlib, seaborn, or plotly, and explain why each choice fits.

Expected

Chart recommendations, Python code, and reasoning for why each chart is appropriate.

Create publication-quality figures

Prompt

Turn the following experimental results into publication-quality figures. Use consistent typography, restrained colors, and well-formatted axes and legends, and provide runnable matplotlib code. Add error bars and subplot layout suggestions if needed.

Expected

Runnable high-quality scientific plotting code with layout and annotation improvement suggestions.

Improve accessible color design

Prompt

I already have a categorical chart, but the colors are hard to distinguish. Redesign the palette based on accessibility and color theory for color-vision-deficient users, and rewrite the example using seaborn or plotly.

Expected

An easier-to-read color palette, updated code, and an explanation of the accessibility improvements.

// docs

Data Visualization Skill

Chart selection guidance, Python visualization code patterns, design principles, and accessibility considerations for creating effective data visualizations.

Chart Selection Guide

Choose by Data Relationship

What You're Showing	Best Chart	Alternatives
Trend over time	Line chart	Area chart (if showing cumulative or composition)
Comparison across categories	Vertical bar chart	Horizontal bar (many categories), lollipop chart
Ranking	Horizontal bar chart	Dot plot, slope chart (comparing two periods)
Part-to-whole composition	Stacked bar chart	Treemap (hierarchical), waffle chart
Composition over time	Stacked area chart	100% stacked bar (for proportion focus)
Distribution	Histogram	Box plot (comparing groups), violin plot, strip plot
Correlation (2 variables)	Scatter plot	Bubble chart (add 3rd variable as size)
Correlation (many variables)	Heatmap (correlation matrix)	Pair plot
Geographic patterns	Choropleth map	Bubble map, hex map
Flow / process	Sankey diagram	Funnel chart (sequential stages)
Relationship network	Network graph	Chord diagram
Performance vs. target	Bullet chart	Gauge (single KPI only)
Multiple KPIs at once	Small multiples	Dashboard with separate charts

When NOT to Use Certain Charts

Pie charts: Avoid unless <6 categories and exact proportions matter less than rough comparison. Humans are bad at comparing angles. Use bar charts instead.
3D charts: Never. They distort perception and add no information.
Dual-axis charts: Use cautiously. They can mislead by implying correlation. Clearly label both axes if used.
Stacked bar (many categories): Hard to compare middle segments. Use small multiples or grouped bars instead.
Donut charts: Slightly better than pie charts but same fundamental issues. Use for single KPI display at most.

Python Visualization Code Patterns

Setup and Style

import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import seaborn as sns
import pandas as pd
import numpy as np

# Professional style setup
plt.style.use('seaborn-v0_8-whitegrid')
plt.rcParams.update({
    'figure.figsize': (10, 6),
    'figure.dpi': 150,
    'font.size': 11,
    'axes.titlesize': 14,
    'axes.titleweight': 'bold',
    'axes.labelsize': 11,
    'xtick.labelsize': 10,
    'ytick.labelsize': 10,
    'legend.fontsize': 10,
    'figure.titlesize': 16,
})

# Colorblind-friendly palettes
PALETTE_CATEGORICAL = ['#4C72B0', '#DD8452', '#55A868', '#C44E52', '#8172B3', '#937860']
PALETTE_SEQUENTIAL = 'YlOrRd'
PALETTE_DIVERGING = 'RdBu_r'

Line Chart (Time Series)

fig, ax = plt.subplots(figsize=(10, 6))

for label, group in df.groupby('category'):
    ax.plot(group['date'], group['value'], label=label, linewidth=2)

ax.set_title('Metric Trend by Category', fontweight='bold')
ax.set_xlabel('Date')
ax.set_ylabel('Value')
ax.legend(loc='upper left', frameon=True)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

# Format dates on x-axis
fig.autofmt_xdate()

plt.tight_layout()
plt.savefig('trend_chart.png', dpi=150, bbox_inches='tight')

Bar Chart (Comparison)

fig, ax = plt.subplots(figsize=(10, 6))

# Sort by value for easy reading
df_sorted = df.sort_values('metric', ascending=True)

bars = ax.barh(df_sorted['category'], df_sorted['metric'], color=PALETTE_CATEGORICAL[0])

# Add value labels
for bar in bars:
    width = bar.get_width()
    ax.text(width + 0.5, bar.get_y() + bar.get_height()/2,
            f'{width:,.0f}', ha='left', va='center', fontsize=10)

ax.set_title('Metric by Category (Ranked)', fontweight='bold')
ax.set_xlabel('Metric Value')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

plt.tight_layout()

…

View full docs ↗