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npx claudepluginhub 8gg-git/claude --plugin dataHow this command is triggered — by the user, by Claude, or both
Slash command
/data:create-viz <data source> [chart type]The summary Claude sees in its command listing — used to decide when to auto-load this command
# /create-viz - Create Visualizations > If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../CONNECTORS.md). Create publication-quality data visualizations using Python. Generates charts from data with best practices for clarity, accuracy, and design. ## Usage ## Workflow ### 1. Understand the Request Determine: - **Data source**: Query results, pasted data, CSV/Excel file, or data to be queried - **Chart type**: Explicitly requested or needs to be recommended - **Purpose**: Exploration, presentation, report, dashboard component - **...
/chart-designDesigns a production-ready chart or data visualization — selects the optimal chart type, applies accessible color palettes, adds annotations, and outputs HTML/CSS/JS using Chart.js or vanilla SVG.
/analyticsAnalyzes data to extract insights, generate visualizations, or perform analytics based on a question and optional approach like SQL or visualization.
/generate-visualizationsGenerates Mermaid diagrams, data charts, token comparisons, and hero images for Medium articles and technical documentation from paths or themes.
/chartDesigns and implements optimal charts for data goals using D3.js, Chart.js, Recharts, or Plotly. Builds responsive, accessible charts/dashboards with TSX components, transformers, tests.
/plotting-fundamentalsCreates quick interactive plots with hvPlot from pandas DataFrames, NumPy arrays, or Xarray datasets.
Share bugs, ideas, or general feedback.
If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
Create publication-quality data visualizations using Python. Generates charts from data with best practices for clarity, accuracy, and design.
/create-viz <data source> [chart type] [additional instructions]
Determine:
If data warehouse is connected and data needs querying:
If data is pasted or uploaded:
If data is from a previous analysis in the conversation:
If the user didn't specify a chart type, recommend one based on the data and question:
| Data Relationship | Recommended Chart |
|---|---|
| Trend over time | Line chart |
| Comparison across categories | Bar chart (horizontal if many categories) |
| Part-to-whole composition | Stacked bar or area chart (avoid pie charts unless <6 categories) |
| Distribution of values | Histogram or box plot |
| Correlation between two variables | Scatter plot |
| Two-variable comparison over time | Dual-axis line or grouped bar |
| Geographic data | Choropleth map |
| Ranking | Horizontal bar chart |
| Flow or process | Sankey diagram |
| Matrix of relationships | Heatmap |
Explain the recommendation briefly if the user didn't specify.
Write Python code using one of these libraries based on the need:
Code requirements:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
# Set professional style
plt.style.use('seaborn-v0_8-whitegrid')
sns.set_palette("husl")
# Create figure with appropriate size
fig, ax = plt.subplots(figsize=(10, 6))
# [chart-specific code]
# Always include:
ax.set_title('Clear, Descriptive Title', fontsize=14, fontweight='bold')
ax.set_xlabel('X-Axis Label', fontsize=11)
ax.set_ylabel('Y-Axis Label', fontsize=11)
# Format numbers appropriately
# - Percentages: '45.2%' not '0.452'
# - Currency: '$1.2M' not '1200000'
# - Large numbers: '2.3K' or '1.5M' not '2300' or '1500000'
# Remove chart junk
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
plt.tight_layout()
plt.savefig('chart_name.png', dpi=150, bbox_inches='tight')
plt.show()
Color:
Typography:
Layout:
Accuracy:
/create-viz Show monthly revenue for the last 12 months as a line chart with the trend highlighted
/create-viz Here's our NPS data by product: [pastes data]. Create a horizontal bar chart ranking products by score.
/create-viz Query the orders table and create a heatmap of order volume by day-of-week and hour