As AI becomes more powerful and widely used, more people rely on it to analyze data and create charts. However, results can be inconsistent and often require repeated prompting. Chart skills solve this by providing structured instructions that help AI agents generate more accurate, consistent, and presentation-ready visualizations. In this guide, you'll discover practical AI chart skills and learn how to choose the right ones for different data visualization tasks.
What are AI charting skills?
AI charting skills help AI agents handle data visualization workflows, from choosing appropriate chart formats to structuring visual reports and presenting key insights. They can support tasks such as creating charts, improving dashboard layouts, and preparing data-driven presentations based on specific requirements. With Kimi, users can apply built-in charting skills or create custom skills for different data analysis and visualization needs.
8 Kimi's built-in skills that simplify chart creation
If you use Kimi, you already have access to several built-in skills that make chart creation and data visualization much easier. These skills can generate charts, diagrams, timelines, dashboards, and other visual outputs from your data with simple prompts. Explore Kimi's built-in chart skills below to see how each one can support different visualization tasks.
| Skill name | Description |
|---|---|
| chart-image | Generate publication-quality PNG chart images from data, supporting line, bar, area, candlestick, pie, and heatmap charts. Triggers when the user asks to visualize data, create a graph, plot a time series, or generate a chart for a report, alert, or dashboard. Runs as a lightweight, headless Node.js process without a browser. |
| code-to-diagram | Analyze codebases and automatically generate architecture diagrams, flowcharts, and org charts. Uses AST parsing to map import dependencies for Python, JS/TS, Go, and Java, outputting Mermaid or SVG files. Triggered when users ask to visualize code architecture, understand dependencies, draw a flowchart, or create a module diagram from source code. |
| data-viz-renderer | Generate self-contained HTML/SVG infographics from JSON data, including stat cards, bar charts, flow diagrams, and mixed dashboards. Offers 8 color palettes and built-in icons with no external dependencies. Triggered when users request data visualization, infographics, charts, or dashboards. |
| gantt-planner | Generate interactive HTML Gantt charts with Critical Path Method (CPM) analysis from task lists and dependencies, highlighting the critical path and calculating float times. Use when the user asks to create a Gantt chart, schedule project tasks, analyze the critical path, visualize dependencies, or mention project timelines, milestones, or task scheduling. |
| process-doc | Document a business process — flowcharts, RACI, and SOPs. Use when formalizing a process that lives in someone's head, building a RACI to clarify who owns what, writing an SOP for a handoff or audit, or capturing the exceptions and edge cases of how work actually gets done. |
| story-map-builder | Generates an interactive HTML user story map visualizing product requirements in an Epic → Feature → Story structure, with MoSCoW priority tagging and Release swimlane grouping. Trigger when the user mentions story mapping, backlog visualization, MoSCoW priority, release planning, or asks to organize requirements into a story map. |
| timeline-builder | Generate beautiful interactive timeline HTML pages from JSON data, with vertical, horizontal, or dual-side layouts, collapsible details, and custom colors. Ideal for project milestones, company histories, or resumes. Triggered when a user mentions timeline, history of events, project milestones, release log, or asks to visualize a chronological sequence of data. |
| sun-path | Analyzes sunlight and shadows for architectural design, generating sun path diagrams, solar position data, shadow plots, and thermal comfort charts. Triggered by requests about sun position, shadow analysis, annual sun hours, or diagrams for a specific location and time. |
How to access built-in chart skills in Kimi?
Kimi lets you use built-in chart skills with simple prompts. Choose the skill you need, describe the type of chart or visualization you want, and Kimi will generate it automatically. Follow these steps to get started.
Step 1: Input a skill command
Type a chart-related skill command, such as /chart-image, in the input box to activate the skill you want to use.
Step 2: Start your task
Explain the data you want to visualize or the type of chart you need. Kimi will apply the selected skill to generate the appropriate chart or visual output.
Example prompt:
Once a skill is selected, Kimi will apply the predefined workflow and generate results according to your prompts, goals, and task requirements.
Step 3: Review your output
Once the chart is ready, review the output to make sure it matches your requirements. You can refine your prompt if needed or use the generated chart in reports, presentations, or dashboards.
8 open-source AI chart skills you need to try
Built-in skills are a great place to start, but they are only part of what's available. Open-source chart skills give you even more options for creating interactive charts, dashboards, diagrams, and other data visualizations across different projects. Explore the open-source chart skills below to discover tools that fit your visualization workflow.
| Skill name | Description | URL |
|---|---|---|
| d3-visualization | Teaches the agent to produce D3 charts and interactive data visualizations. A comprehensive D3.js skill with examples across chart types and techniques, giving the agent expert-level knowledge to generate complex, interactive visualizations for editorial dashboards, reports, and explanatory graphics. | https://github.com/jiannanya/snow-d3 |
| openchart | Generate charts, tables, graphs, and Sankey diagrams with OpenChart. Part of the OpenData platform's official agent skills for data visualizations and design, built on the Agent Skills spec. | https://github.com/tryopendata/skills |
| data-visualization | Enables developers to create clear, effective data visualizations using matplotlib and seaborn for exploratory analysis and stakeholder communication. Ideal for transforming complex data into insights through histograms, scatter plots, and heatmaps. | https://github.com/aj-geddes/useful-ai-prompts |
| mckinsey-style-visualization | Agent skill that turns messy notes, metrics, and prose into executive-ready visualizations, including board slides, reports, proposals, training materials, technical diagrams, and infographics with a built-in renderer that produces real SVG slides. | https://github.com/kgraph57/mckinsey-style-visualization-skill |
| preview-d3 | Interactive 2D data visualizations as part of the preview skills suite. Standalone tool that renders visual previews directly in the browser with no servers or external dependencies, just self-contained HTML files that open instantly. | https://github.com/veelenga/preview-skills |
| markdown-viewer-skills | Opinionated skills for AI coding agents to create stunning diagrams and visualizations directly in Markdown. 14 skills covering 5 rendering engines from software modeling to enterprise architecture, data analytics, and editorial quality content cards. | https://github.com/markdown-viewer/skills |
| svg-design | Create SVG logos, icons, and graphics as part of the OpenDesign plugin. Part of the official OpenData platform agent skills for visual design and charting elements. | https://github.com/tryopendata/skills |
| snow-d3 | Comprehensive D3.js skills with examples across chart types and techniques. Gives the agent expert-level knowledge to generate complex, interactive visualizations including zoomable treemaps, force-directed network graphs, and calendar heatmaps. | https://github.com/jiannanya/snow-d3 |
How to expand Kimi with external AI chart skills?
You can add open-source chart skills to Kimi by sharing the GitHub repository of the skill you want to use. Once installed, the skill becomes available in your workspace and can be used for future charting and visualization tasks. Here's how you can do it.
Step 1: Enter a prompt
Open Kimi and ask it to install the chart skill using the GitHub URL of the repository.
Example prompt:
Step 2: Let AI install the skill automatically
Kimi downloads the required files, configures the skill, and prepares it for use. Once the installation is complete, the new chart skill can be easily added to your available skills.
Step 3: Use the skill
Select the installed skill, activate it, and describe the chart or visualization you want to create. Kimi will apply the skill to generate the requested visual output.
Take data visualization further with custom skills in Kimi
Kimi also lets you create your own chart skills from existing documents and resources. This is useful when you want AI to follow your preferred charting methods, formatting rules, or reporting standards for future visualization tasks. Follow these steps to build custom skills in Kimi.
Step 1: Access the document to the skills tool
Open Kimi Skills and choose "Document to skills" to begin creating your custom chart skill.
Step 2: Upload the files
Upload the resources you want the skill to learn from, such as charting guidelines, dashboard templates, visualization standards, reporting documents, or data formatting instructions. Kimi will organize the content into a reusable skill.
Step 3: Create and use your skills
After processing the files, Kimi creates a custom skill that you can use to generate charts and visualizations following your preferred style and workflow. You can also update the skill later or export it for use in other projects.
Edit your skill anytime to improve instructions, update workflows, and optimize results. Once finalized, save it for reuse, sharing, or managing across different tasks.
How to use AI chart skills that work better?
Creating a chart is only part of the process. The quality of your data, prompts, and instructions has a big impact on how useful the final visualization will be. The following tips will help you use AI charting skills more effectively and generate clearer, more meaningful charts.
Define your visualization goal clearly
Before using chart skills, decide what you want the chart to show, such as trends, comparisons, relationships, or overall performance. A clear objective helps AI choose the most appropriate visualization. For example, trend analysis works best with a line chart, while category comparisons are better shown with a bar chart.
Prepare clean and structured data
Well-organized data leads to more accurate and reliable charts. Make sure your dataset has clear column names, consistent formatting, complete values, and no duplicate or incorrect entries. Clean data helps AI generate visualizations that are easier to understand.
Choose the right chart type with AI assistance
Each chart type is designed for a different purpose, so avoid using the same format for every dataset. Ask AI to recommend the most suitable visualization based on your data and goals. This helps you select charts that communicate information more effectively.
Write detailed prompts for customized results
Specific instructions help AI create charts that better match your needs. Instead of simply requesting "create a sales chart," provide details about the chart format, data range, audience, and important elements to highlight. You can ask AI to add titles, labels, or annotations to make the final visualization more informative.
Use AI to extract insights from charts
AI chart skills can do more than generate visuals. They can help interpret the data behind them. After creating a chart, ask AI to identify trends, unusual changes, potential causes, and actionable insights. This transforms charts from simple illustrations into tools that support analysis and decision-making.
Refine charts through multiple iterations
The first generated chart may not always be the most effective version. Review the output and provide follow-up instructions to improve readability, adjust layouts, simplify complex information, or emphasize specific data points. Iterative refinement helps you create polished charts suitable for reports and presentations.
Conclusion
Good visualizations make data easier to understand, share, and act on. The right charting skills can simplify repetitive work while helping you create more consistent and professional results across different projects. As your needs grow, you can also customize these skills to match your own workflow and reporting style. Try Kimi to explore built-in and custom chart skills for faster, smarter data visualization.