chart

To create a chart, provide your numeric inputs to the agent and specify the desired visualization type, such as a bar chart or line chart. The agent generates a PNG image artifact using Matplotlib and returns it for immediate use in your reports or dashboards.

You can generate multiple data visualizations including bar charts, line charts, pie or donut charts, scatter plots, heatmaps, histograms, and stacked bars. These options cover common needs like comparing performance, showing distributions, and summarizing composition.

You generate a heatmap by providing your numeric matrix data to the agent and requesting a heatmap visualization. The agent uses Matplotlib to process the numeric inputs and returns a PNG image artifact that visually represents the density or distribution of your values.

Yes, you can use a code interpreter to create a bar chart. The agent executes Matplotlib code in a single call, automatically uploading the generated chart to S3 and reporting the resulting artifact reference for you to access and share.

The best way to compare series across categories is by using bar charts or stacked bars. This Skill processes your numeric inputs with Matplotlib to create visual comparisons, outputting a PNG image artifact that clearly highlights performance differences across your categories.

No, the chart generation process does not require a display environment. It enforces headless execution using the Matplotlib Agg backend, allowing you to generate data visualizations and save them as PNG artifacts in server-side or automated reporting scenarios.