

Ultracite is a production-grade, zero-configuration preset for ESLint, Biome, Oxlint, Prettier, and Stylelint designed to help developers and AI models write consistent and type-safe code without the hassle of configuration. It serves as a comprehensive code quality tool that enforces coding standards across projects.
Ultracite offers hundreds of rules optimized for JavaScript and TypeScript code across various frameworks. It provides agent integration for AI models, editor configuration files for popular IDEs, configurable specifications for Biome, and MCP support for linting and formatting code without local installation. The tool generates rules files for popular IDEs and agents to maximize AI integration effectiveness.
The product works through a zero-configuration approach where users can quickly initialize it using simple commands like 'bun x ultracite@latest init'. It automatically detects project configurations and provides framework-specific rulesets. Ultracite integrates with development workflows through automated setup processes that configure linters and formatters based on project requirements.
Ultracite ensures consistent code style and quality across all team members and AI models, eliminating debates over formatting and reducing code review friction. It helps developers ship code faster by automating code quality checks and providing immediate feedback through integrated development environments.
Ultracite is designed for software developers, engineering teams, and organizations using JavaScript/TypeScript frameworks like React and Next.js. It integrates with popular AI coding assistants including Claude Code, Codex, Gemini, Warp, OpenCode, Qwen Code, and GitHub Copilot, supporting both human developers and AI-powered coding workflows.
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Ultracite is designed for software developers, engineering teams, and organizations using JavaScript/TypeScript frameworks like React and Next.js. It serves developers who want to maintain high code quality standards without spending time on configuration. The tool is particularly valuable for teams working with AI coding assistants and those requiring consistent coding standards across multiple contributors.