chartjs-performance-tips
About
This skill provides performance optimization techniques for Chart.js implementations, focusing on rendering efficiency with large datasets. Key tips include disabling animations, implementing data decimation, and managing canvas resources. Use it when building data visualizations that need to maintain responsiveness with substantial data volumes.
Quick Install
Claude Code
Recommendednpx skills add vamseeachanta/workspace-hub -a claude-code/plugin add https://github.com/vamseeachanta/workspace-hubgit clone https://github.com/vamseeachanta/workspace-hub.git ~/.claude/skills/chartjs-performance-tipsCopy and paste this command in Claude Code to install this skill
GitHub Repository
Related Skills
d3js
OtherThis skill enables developers to create fully customized, interactive data visualizations using D3.js, offering complete control over SVG elements and data binding. It's ideal for building unique, complex charts with sophisticated transitions and interactions that standard libraries can't achieve. Use it when you need bespoke, data-driven visualizations rather than simple, pre-styled charts.
plotly-javascript-cdn
OtherThis skill provides the CDN script tag to embed Plotly.js directly into web projects for interactive data visualizations. It's ideal for developers needing quick client-side charting without build tools or npm dependencies. The skill also includes complementary installation commands for Python/R Plotly backends.
highcharts-theming
OtherThis skill enables developers to apply custom visual themes to Highcharts visualizations. It provides reference code for globally configuring colors, backgrounds, fonts, and other styling options using `Highcharts.setOptions()`. Use this skill when you need to maintain consistent branding or styling across multiple charts in your application.
highcharts-boost-module-large-datasets
OtherThis Highcharts skill enables rendering large datasets (thousands+ points) using WebGL acceleration for improved performance. It includes the Boost module for GPU-optimized rendering along with annotations and drill-down capabilities for interactive data exploration. Use this when visualizing massive datasets where standard rendering would cause performance issues.
