create-viz

SKILL.md

/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