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visualization-specialist

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Creates data visualizations, charts, and interactive dashboards. Invoke when user wants to create plots, graphs, or visual representations of data.

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Source SKILL.md: https://github.com/liangdabiao/claude-data-analysis-ultra-main/blob/HEAD/.claude/skills/visualization-specialist/SKILL.md

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Visualization Specialist

Expert data visualization specialist for creating interactive, insightful, and publication-quality visualizations.

When to Invoke This Skill

Invoke this skill when user:

  • Wants to create data visualizations or charts
  • Needs to visualize patterns or trends
  • Wants interactive dashboards
  • Needs publication-quality plots
  • Asks for specific chart types (bar, line, scatter, etc.)
  • Needs data story telling through visuals
  • Specifies a chart type (all, trends, distribution, correlation, comparison)

Chart Types (Advanced Mode)

用户可以指定图表类型：

1. all (完整仪表板)

创建包含多种图表类型的综合仪表板：

  • 数据概览
  • 关键变量可视化
  • 交互式探索仪表板

2. trends (趋势分析)

时间序列相关图表：

  • 折线图
  • 移动平均图
  • 趋势分解图
  • 季节性分析图

3. distribution (分布分析)

分布相关图表：

  • 直方图
  • 密度图
  • 箱线图
  • 小提琴图

4. correlation (相关性分析)

相关性可视化：

  • 散点图
  • 相关性热力图
  • 配对图

5. comparison (对比分析)

对比类图表：

  • 分组条形图
  • 堆叠条形图
  • 对比折线图

6. custom (自定义)

根据用户需求创建特定图表

Core Capabilities

Visualization Types

  • Statistical Charts: Histograms, box plots, scatter plots, correlation matrices
  • Time Series: Line charts, area charts, candlestick charts
  • Categorical Data: Bar charts, pie charts, heatmaps, treemaps
  • Distribution Analysis: Density plots, violin plots, Q-Q plots
  • Multivariate Data: Parallel coordinates, radar charts, bubble charts
  • Geographic Data: Choropleth maps, point maps
  • Comparative Analysis: Side-by-side charts, small multiples

Design Principles

  • Data-Ink Ratio: Maximize data-ink, minimize chart junk
  • Color Theory: Use appropriate, accessible color schemes
  • Accessibility: Ensure colorblind-friendly designs
  • Labeling: Clear, concise labels and titles
  • Scale: Appropriate scaling for data

Technical Skills

  • Matplotlib/Seaborn: Static visualizations
  • Plotly: Interactive web visualizations
  • Pandas: Built-in plotting

Chart Selection Guide

For Numerical Data

  • Distribution: Histogram, box plot, violin plot, density plot
  • Comparison: Bar chart, line chart, scatter plot
  • Relationship: Scatter plot, correlation heatmap
  • Trend: Line chart, area chart

For Categorical Data

  • Frequency: Bar chart, pie chart
  • Comparison: Grouped bar chart, stacked bar chart
  • Relationship: Heatmap, mosaic plot

For Time Series

  • Trend: Line chart, area chart
  • Seasonality: Seasonal decomposition
  • Comparison: Multiple line charts

Chinese Font Support

IMPORTANT: When creating visualizations with Chinese text, always configure proper fonts:

import matplotlib.pyplot as plt
import matplotlib

# Windows
matplotlib.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'PingFang SC']
# Mac
matplotlib.rcParams['font.sans-serif'] = ['PingFang SC', 'Heiti SC', 'Arial Unicode MS']
# Linux
matplotlib.rcParams['font.sans-serif'] = ['WenQuanYi Micro Hei', 'SimHei']

# Must have this to show minus signs correctly
matplotlib.rcParams['axes.unicode_minus'] = False

Usage Examples

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Configure Chinese font
plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei']
plt.rcParams['axes.unicode_minus'] = False

# Load data
df = pd.read_csv('./data_storage/your_data.csv')

# Create visualization
fig, ax = plt.subplots(figsize=(10, 6))
sns.histplot(data=df, x='column_name', kde=True, ax=ax)
ax.set_title('数据分布图', fontsize=14)
ax.set_xlabel('列名', fontsize=12)
ax.set_ylabel('频数', fontsize=12)
plt.tight_layout()
plt.savefig('./visualizations/distribution.png', dpi=300, bbox_inches='tight')

Output Standards

File Formats

  • Static Images: PNG (300 dpi), SVG, PDF
  • Interactive: HTML (Plotly)
  • Output Directory: ./visualizations/

Quality Requirements

  • High resolution (300 dpi for static)
  • Proper Chinese labels and titles
  • Clear legends and annotations
  • Consistent color schemes
  • Responsive layout

Collaboration

Work with other skills:

  • data-explorer: Get statistical insights to visualize
  • report-writer: Supply visualizations for reports
  • code-generator: Generate reusable plotting code

Language

All visualization labels, titles, and annotations must be in Chinese:

  • Chart titles
  • Axis labels
  • Legend text
  • Annotations and tooltips