data-visualization

Name: data-visualization
Author: anthropics

10kinstalls2.7kforks

How to install data-visualization?

anthropics/knowledge-work-plugins/data-visualization

$npx -y skills add anthropics/knowledge-work-plugins --skill data-visualization

Installs into the current project.

Summary

data-visualization is an agent skill published from anthropics/knowledge-work-plugins, installable through the skills CLI.

Files · 1

View on GitHub

SKILL.md

1	# Data Visualization Skill
2
3	Chart selection guidance, Python visualization code patterns, design principles, and accessibility considerations for creating effective data visualizations.
4
5	## Chart Selection Guide
6
7	### Choose by Data Relationship
8
9	\| What You're Showing \| Best Chart \| Alternatives \|
10	\|---\|---\|---\|
11	\| Trend over time \| Line chart \| Area chart (if showing cumulative or composition) \|
12	\| Comparison across categories \| Vertical bar chart \| Horizontal bar (many categories), lollipop chart \|
13	\| Ranking \| Horizontal bar chart \| Dot plot, slope chart (comparing two periods) \|
14	\| Part-to-whole composition \| Stacked bar chart \| Treemap (hierarchical), waffle chart \|
15	\| Composition over time \| Stacked area chart \| 100% stacked bar (for proportion focus) \|
16	\| Distribution \| Histogram \| Box plot (comparing groups), violin plot, strip plot \|
17	\| Correlation (2 variables) \| Scatter plot \| Bubble chart (add 3rd variable as size) \|
18	\| Correlation (many variables) \| Heatmap (correlation matrix) \| Pair plot \|
19	\| Geographic patterns \| Choropleth map \| Bubble map, hex map \|
20	\| Flow / process \| Sankey diagram \| Funnel chart (sequential stages) \|
21	\| Relationship network \| Network graph \| Chord diagram \|
22	\| Performance vs. target \| Bullet chart \| Gauge (single KPI only) \|
23	\| Multiple KPIs at once \| Small multiples \| Dashboard with separate charts \|
24
25	### When NOT to Use Certain Charts
26
27	- Pie charts: Avoid unless <6 categories and exact proportions matter less than rough comparison. Humans are bad at comparing angles. Use bar charts instead.
28	- 3D charts: Never. They distort perception and add no information.
29	- Dual-axis charts: Use cautiously. They can mislead by implying correlation. Clearly label both axes if used.
30	- Stacked bar (many categories): Hard to compare middle segments. Use small multiples or grouped bars instead.
31	- Donut charts: Slightly better than pie charts but same fundamental issues. Use for single KPI display at most.
32
33	## Python Visualization Code Patterns
34
35	### Setup and Style
36
37	```python
38	import matplotlib.pyplot as plt
39	import matplotlib.ticker as mticker
40	import seaborn as sns
41	import pandas as pd
42	import numpy as np
43
44	# Professional style setup
45	plt.style.use('seaborn-v0_8-whitegrid')
46	plt.rcParams.update({
47	'figure.figsize': (10, 6),
48	'figure.dpi': 150,
49	'font.size': 11,
50	'axes.titlesize': 14,
51	'axes.titleweight': 'bold',
52	'axes.labelsize': 11,
53	'xtick.labelsize': 10,
54	'ytick.labelsize': 10,
55	'legend.fontsize': 10,
56	'figure.titlesize': 16,
57	})
58
59	# Colorblind-friendly palettes
60	PALETTE_CATEGORICAL = ['#4C72B0', '#DD8452', '#55A868', '#C44E52', '#8172B3', '#937860']
61	PALETTE_SEQUENTIAL = 'YlOrRd'
62	PALETTE_DIVERGING = 'RdBu_r'
63	```
64
65	### Line Chart (Time Series)
66
67	```python
68	fig, ax = plt.subplots(figsize=(10, 6))
69
70	for label, group in df.groupby('category'):
71	ax.plot(group['date'], group['value'], label=label, linewidth=2)
72
73	ax.set_title('Metric Trend by Category', fontweight='bold')
74	ax.set_xlabel('Date')
75	ax.set_ylabel('Value')
76	ax.legend(loc='upper left', frameon=True)
77	ax.spines['top'].set_visible(False)
78	ax.spines['right'].set_visible(False)
79
80	# Format dates on x-axis
81	fig.autofmt_xdate()
82
83	plt.tight_layout()
84	plt.savefig('trend_chart.png', dpi=150, bbox_inches='tight')
85	```
86
87	### Bar Chart (Comparison)
88
89	```python
90	fig, ax = plt.subplots(figsize=(10, 6))
91
92	# Sort by value for easy reading
93	df_sorted = df.sort_values('metric', ascending=True)
94
95	bars = ax.barh(df_sorted['category'], df_sorted['metric'], color=PALETTE_CATEGORICAL[0])
96
97	# Add value labels
98	for bar in bars:
99	width = bar.get_width()
100	ax.text(width + 0.5, bar.get_y() + bar.get_height()/2,
101	f'{width:,.0f}', ha='left', va='center', fontsize=10)
102
103	ax.set_title('Metric by Category (Ranked)', fontweight='bold')
104	ax.set_xlabel('Metric Value')
105	ax.spines['top'].set_visible(False)
106	ax.spines['right'].set_visible(False)
107
108	plt.tight_layout()
109	plt.savefig('bar_chart.png', dpi=150, bbox_inches='tight')
110	```
111
112	### Histogram (Distribution)
113
114	```python
115	fig, ax = plt.subplots(figsize=(10, 6))
116
117	ax.hist(df['value'], bins=30, color=PALETTE_CATEGORICAL[0], edgecolor='white', alpha=0.8)
118
119	# Add mean and median lines
120	mean_val = df['value'].mean()
121	median_val = df['value'].median()
122	ax.axvline(mean_val, color='red', linestyle='--', linewidth=1.5, label=f'Mean: {mean_val:,.1f}')
123	ax.axvline(median_val, color='green', linestyle='--', linewidth=1.5, label=f'Median: {median_val:,.1f}')
124
125	ax.set_title('Distribution of Values', fontweight='bold')
126	ax.set_xlabel('Value')
127	ax.set_ylabel('Frequency')
128	ax.legend()
129	ax.spines['top'].set_visible(False)
130	ax.spines['right'].set_visible(False)
131
132	plt.ti

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data-visualization

10kinstalls2.7kforks

View on GitHub

How to install data-visualization?

anthropics/knowledge-work-plugins/data-visualization

$npx -y skills add anthropics/knowledge-work-plugins --skill data-visualization

Installs into the current project.

Summary

data-visualization is an agent skill published from anthropics/knowledge-work-plugins, installable through the skills CLI.

Files · 1

View on GitHub

SKILL.md

1	# Data Visualization Skill
2
3	Chart selection guidance, Python visualization code patterns, design principles, and accessibility considerations for creating effective data visualizations.
4
5	## Chart Selection Guide
6
7	### Choose by Data Relationship
8
9	\| What You're Showing \| Best Chart \| Alternatives \|
10	\|---\|---\|---\|
11	\| Trend over time \| Line chart \| Area chart (if showing cumulative or composition) \|
12	\| Comparison across categories \| Vertical bar chart \| Horizontal bar (many categories), lollipop chart \|
13	\| Ranking \| Horizontal bar chart \| Dot plot, slope chart (comparing two periods) \|
14	\| Part-to-whole composition \| Stacked bar chart \| Treemap (hierarchical), waffle chart \|
15	\| Composition over time \| Stacked area chart \| 100% stacked bar (for proportion focus) \|
16	\| Distribution \| Histogram \| Box plot (comparing groups), violin plot, strip plot \|
17	\| Correlation (2 variables) \| Scatter plot \| Bubble chart (add 3rd variable as size) \|
18	\| Correlation (many variables) \| Heatmap (correlation matrix) \| Pair plot \|
19	\| Geographic patterns \| Choropleth map \| Bubble map, hex map \|
20	\| Flow / process \| Sankey diagram \| Funnel chart (sequential stages) \|
21	\| Relationship network \| Network graph \| Chord diagram \|
22	\| Performance vs. target \| Bullet chart \| Gauge (single KPI only) \|
23	\| Multiple KPIs at once \| Small multiples \| Dashboard with separate charts \|
24
25	### When NOT to Use Certain Charts
26
27	- Pie charts: Avoid unless <6 categories and exact proportions matter less than rough comparison. Humans are bad at comparing angles. Use bar charts instead.
28	- 3D charts: Never. They distort perception and add no information.
29	- Dual-axis charts: Use cautiously. They can mislead by implying correlation. Clearly label both axes if used.
30	- Stacked bar (many categories): Hard to compare middle segments. Use small multiples or grouped bars instead.
31	- Donut charts: Slightly better than pie charts but same fundamental issues. Use for single KPI display at most.
32
33	## Python Visualization Code Patterns
34
35	### Setup and Style
36
37	```python
38	import matplotlib.pyplot as plt
39	import matplotlib.ticker as mticker
40	import seaborn as sns
41	import pandas as pd
42	import numpy as np
43
44	# Professional style setup
45	plt.style.use('seaborn-v0_8-whitegrid')
46	plt.rcParams.update({
47	'figure.figsize': (10, 6),
48	'figure.dpi': 150,
49	'font.size': 11,
50	'axes.titlesize': 14,
51	'axes.titleweight': 'bold',
52	'axes.labelsize': 11,
53	'xtick.labelsize': 10,
54	'ytick.labelsize': 10,
55	'legend.fontsize': 10,
56	'figure.titlesize': 16,
57	})
58
59	# Colorblind-friendly palettes
60	PALETTE_CATEGORICAL = ['#4C72B0', '#DD8452', '#55A868', '#C44E52', '#8172B3', '#937860']
61	PALETTE_SEQUENTIAL = 'YlOrRd'
62	PALETTE_DIVERGING = 'RdBu_r'
63	```
64
65	### Line Chart (Time Series)
66
67	```python
68	fig, ax = plt.subplots(figsize=(10, 6))
69
70	for label, group in df.groupby('category'):
71	ax.plot(group['date'], group['value'], label=label, linewidth=2)
72
73	ax.set_title('Metric Trend by Category', fontweight='bold')
74	ax.set_xlabel('Date')
75	ax.set_ylabel('Value')
76	ax.legend(loc='upper left', frameon=True)
77	ax.spines['top'].set_visible(False)
78	ax.spines['right'].set_visible(False)
79
80	# Format dates on x-axis
81	fig.autofmt_xdate()
82
83	plt.tight_layout()
84	plt.savefig('trend_chart.png', dpi=150, bbox_inches='tight')
85	```
86
87	### Bar Chart (Comparison)
88
89	```python
90	fig, ax = plt.subplots(figsize=(10, 6))
91
92	# Sort by value for easy reading
93	df_sorted = df.sort_values('metric', ascending=True)
94
95	bars = ax.barh(df_sorted['category'], df_sorted['metric'], color=PALETTE_CATEGORICAL[0])
96
97	# Add value labels
98	for bar in bars:
99	width = bar.get_width()
100	ax.text(width + 0.5, bar.get_y() + bar.get_height()/2,
101	f'{width:,.0f}', ha='left', va='center', fontsize=10)
102
103	ax.set_title('Metric by Category (Ranked)', fontweight='bold')
104	ax.set_xlabel('Metric Value')
105	ax.spines['top'].set_visible(False)
106	ax.spines['right'].set_visible(False)
107
108	plt.tight_layout()
109	plt.savefig('bar_chart.png', dpi=150, bbox_inches='tight')
110	```
111
112	### Histogram (Distribution)
113
114	```python
115	fig, ax = plt.subplots(figsize=(10, 6))
116
117	ax.hist(df['value'], bins=30, color=PALETTE_CATEGORICAL[0], edgecolor='white', alpha=0.8)
118
119	# Add mean and median lines
120	mean_val = df['value'].mean()
121	median_val = df['value'].median()
122	ax.axvline(mean_val, color='red', linestyle='--', linewidth=1.5, label=f'Mean: {mean_val:,.1f}')
123	ax.axvline(median_val, color='green', linestyle='--', linewidth=1.5, label=f'Median: {median_val:,.1f}')
124
125	ax.set_title('Distribution of Values', fontweight='bold')
126	ax.set_xlabel('Value')
127	ax.set_ylabel('Frequency')
128	ax.legend()
129	ax.spines['top'].set_visible(False)
130	ax.spines['right'].set_visible(False)
131
132	plt.ti

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