Create Viz Skill for Claude

/create-viz - Create Visualizations

> 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.

Usage

/create-viz <data source> [chart type] [additional instructions]

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
• Audience: Technical team, executives, external stakeholders

2. Get the Data

If data warehouse is connected and data needs querying:

• Write and execute the query
• Load results into a pandas DataFrame

If data is pasted or uploaded:

• Parse the data into a pandas DataFrame
• Clean and prepare as needed (type conversions, null handling)

If data is from a previous analysis in the conversation:

• Reference the existing data

3. Select Chart Type

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.

4. Generate the Visualization

Write Python code using one of these libraries based on the need:

• matplotlib + seaborn: Best for static, publication-quality charts. Default choice.
• plotly: Best for interactive charts or when the user requests interactivity.

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()

5. Apply Design Best Practices

Color:

• Use a consistent, colorblind-friendly palette
• Use color meaningfully (not decoratively)
• Highlight the key data point or trend with a contrasting color
• Grey out less important reference data

Typography:

• Descriptive title that states the insight, not just the metric (e.g., "Revenue grew 23% YoY" not "Revenue by Month")
• Readable axis labels (not rotated 90 degrees if avoidable)
• Data labels on key points when they add clarity

Layout:

• Appropriate whitespace and margins
• Legend placement that doesn't obscure data
• Sorted categories by value (not alphabetically) unless there's a natural order

Accuracy:

• Y-axis starts at zero for bar charts
• No misleading axis breaks without clear notation
• Consistent scales when comparing panels
• Appropriate precision (don't show 10 decimal places)

6. Save and Present

• Save the chart as a PNG file with descriptive name
• Display the chart to the user
• Provide the code used so they can modify it
• Suggest variations (different chart type, different grouping, zoomed time range)

Examples

/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

Tips

• If you want interactive charts (hover, zoom, filter), mention "interactive" and Claude will use plotly
• Specify "presentation" if you need larger fonts and higher contrast
• You can request multiple charts at once (e.g., "create a 2x2 grid of charts showing...")
• Charts are saved to your current directory as PNG files