/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 [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