openclaw/skills/data-viz/SKILL.md

name	description	homepage	metadata
data-viz	Generate charts and data visualizations using Python matplotlib. Use when asked to create graphs, charts, plots, or visual representations of data. Supports line charts, bar charts, pie charts, scatter plots, heatmaps, and more.	https://matplotlib.org/	openclaw emoji requires 📊 bins uv

Data Visualization Skill

Generate professional charts and graphs using Python matplotlib via uv run (no pip install needed).

Quick Start

uv run --with matplotlib python3 << 'EOF'
import matplotlib.pyplot as plt
plt.style.use('dark_background')

data = [10, 25, 15, 30, 20]
labels = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri']

fig, ax = plt.subplots(figsize=(10, 5))
ax.plot(labels, data, 'o-', color='#e94560', linewidth=2)
ax.fill_between(labels, data, alpha=0.3, color='#e94560')
ax.set_title('Weekly Metrics', fontsize=14, fontweight='bold')
ax.grid(True, alpha=0.3)
plt.tight_layout()
plt.savefig('chart.png', dpi=150, facecolor='#1a1a2e')
print('✅ Saved: chart.png')
EOF

Chart Types

Line Chart (Time Series)

uv run --with matplotlib python3 << 'EOF'
import matplotlib.pyplot as plt
plt.style.use('dark_background')

dates = ['Jan 1', 'Jan 2', 'Jan 3', 'Jan 4', 'Jan 5']
values = [10, 25, 15, 30, 20]

fig, ax = plt.subplots(figsize=(12, 5))
ax.plot(dates, values, 'o-', color='#e94560', linewidth=2, markersize=6)
ax.fill_between(dates, values, alpha=0.3, color='#e94560')
ax.set_title('Daily Metrics', fontsize=14, fontweight='bold')
ax.set_xlabel('Date')
ax.set_ylabel('Value')
ax.grid(True, alpha=0.3)
plt.tight_layout()
plt.savefig('line_chart.png', dpi=150, facecolor='#1a1a2e')
print('✅ Saved: line_chart.png')
EOF

Bar Chart

uv run --with matplotlib python3 << 'EOF'
import matplotlib.pyplot as plt
plt.style.use('dark_background')

categories = ['Service A', 'Service B', 'Service C', 'Service D']
values = [45, 32, 67, 28]
colors = ['#e94560', '#27ae60', '#3498db', '#f39c12']

fig, ax = plt.subplots(figsize=(10, 5))
bars = ax.bar(categories, values, color=colors, alpha=0.8)
ax.set_title('Comparison', fontsize=14, fontweight='bold')
ax.set_ylabel('Count')

# Add value labels on bars
for bar, val in zip(bars, values):
    ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 1,
            str(val), ha='center', fontsize=10)

plt.tight_layout()
plt.savefig('bar_chart.png', dpi=150, facecolor='#1a1a2e')
print('✅ Saved: bar_chart.png')
EOF

Dual-Line Chart (Comparison)

uv run --with matplotlib python3 << 'EOF'
import matplotlib.pyplot as plt
plt.style.use('dark_background')

dates = ['Jan 1', 'Jan 2', 'Jan 3', 'Jan 4', 'Jan 5']
series_a = [71, 7, 22, 35, 26]
series_b = [50, 6, 11, 27, 21]

fig, ax = plt.subplots(figsize=(12, 5))
ax.fill_between(dates, series_a, alpha=0.3, color='#e94560')
ax.fill_between(dates, series_b, alpha=0.3, color='#27ae60')
ax.plot(dates, series_a, 'o-', color='#e94560', linewidth=2, label='Series A')
ax.plot(dates, series_b, 's-', color='#27ae60', linewidth=2, label='Series B')
ax.set_title('Comparison', fontsize=14, fontweight='bold')
ax.legend(loc='upper right')
ax.grid(True, alpha=0.3)
plt.tight_layout()
plt.savefig('dual_chart.png', dpi=150, facecolor='#1a1a2e')
print('✅ Saved: dual_chart.png')
EOF

Pie Chart

uv run --with matplotlib python3 << 'EOF'
import matplotlib.pyplot as plt
plt.style.use('dark_background')

labels = ['Category A', 'Category B', 'Category C', 'Other']
sizes = [580, 324, 154, 200]
colors = ['#e94560', '#27ae60', '#3498db', '#95a5a6']
explode = (0.05, 0, 0, 0)  # Highlight first slice

fig, ax = plt.subplots(figsize=(8, 8))
ax.pie(sizes, explode=explode, labels=labels, colors=colors,
       autopct='%1.1f%%', shadow=True, startangle=90)
ax.set_title('Distribution', fontsize=14, fontweight='bold')
plt.tight_layout()
plt.savefig('pie_chart.png', dpi=150, facecolor='#1a1a2e')
print('✅ Saved: pie_chart.png')
EOF

Heatmap

uv run --with matplotlib --with numpy python3 << 'EOF'
import matplotlib.pyplot as plt
import numpy as np
plt.style.use('dark_background')

# 4 weeks x 7 days of random data
data = np.random.randint(10, 100, (4, 7))
days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
weeks = ['Week 1', 'Week 2', 'Week 3', 'Week 4']

fig, ax = plt.subplots(figsize=(10, 4))
im = ax.imshow(data, cmap='RdYlGn_r')
ax.set_xticks(range(7))
ax.set_yticks(range(4))
ax.set_xticklabels(days)
ax.set_yticklabels(weeks)
plt.colorbar(im, label='Value')
ax.set_title('Heatmap', fontsize=14, fontweight='bold')
plt.tight_layout()
plt.savefig('heatmap.png', dpi=150, facecolor='#1a1a2e')
print('✅ Saved: heatmap.png')
EOF

Loading Data from JSON

uv run --with matplotlib python3 << 'EOF'
import json
import matplotlib.pyplot as plt
from pathlib import Path

# Load data from JSON file
with open('data.json') as f:
    data = json.load(f)

labels = data['labels']
values = data['values']

plt.style.use('dark_background')
fig, ax = plt.subplots(figsize=(12, 5))
ax.bar(labels, values, color='#e94560', alpha=0.8)
ax.set_title(data.get('title', 'Chart'), fontsize=14, fontweight='bold')
ax.grid(True, alpha=0.3, axis='y')
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.savefig('chart.png', dpi=150, facecolor='#1a1a2e')
print('✅ Saved: chart.png')
EOF

Style Options

Dark Theme (recommended for chat)

plt.style.use('dark_background')
plt.savefig('chart.png', facecolor='#1a1a2e')

Light Theme

plt.style.use('default')
plt.savefig('chart.png', facecolor='white')

Color Palette

COLORS = {
    'red': '#e94560',
    'green': '#27ae60',
    'blue': '#3498db',
    'orange': '#f39c12',
    'purple': '#9b59b6',
    'teal': '#1abc9c',
    'gray': '#95a5a6'
}

Tips

1. Use uv run --with matplotlib — installs matplotlib on-the-fly, no pip needed
2. Dark theme looks better in chat interfaces
3. Always add labels/legends — charts should be self-explanatory
4. Use figsize — (12, 5) for wide charts, (8, 8) for square
5. Set dpi=150 for crisp images without being too large
6. Read the saved PNG after generating to display to user

Requirements

• uv — Python package runner (install: curl -LsSf https://astral.sh/uv/install.sh | sh)