Rules that teach
AI agents your way.

Agent rules are configuration files that tell AI coding assistants like Cursor, Claude Code, GitHub Copilot, and Windsurf how to write code for your specific project. They define your tech stack, coding conventions, architecture patterns, and preferences so the AI generates code that matches your style — instead of generic output. Think of them as a style guide that your AI pair programmer reads before writing any code.

A .cursorrules file is a configuration file placed at the root of your project that tells the Cursor AI editor how to generate code for that project. It can contain instructions about your tech stack, naming conventions, preferred libraries, architectural patterns, and coding style. Cursor reads this file automatically whenever it generates or edits code, ensuring consistent output that matches your project's standards.

CLAUDE.md is a Markdown file placed at the root of your project that provides instructions to Claude Code (Anthropic's CLI coding tool). Claude Code automatically reads this file when working on your project. You can include your tech stack, code conventions, project structure, and any specific instructions. It's also commonly used as a convention when working with Claude through other interfaces like the API or web.

copilot-instructions.md is a file placed at .github/copilot-instructions.md that configures GitHub Copilot's behavior for your repository. It tells Copilot about your project's conventions, preferred patterns, and coding standards. GitHub Copilot reads this file automatically and applies the instructions when generating code suggestions, completions, and chat responses within VS Code or other supported editors.

The fastest way is to use the Agent Rules Builder — select your language, framework, and AI coding assistant, and it generates a tailored rules file in seconds using 1000+ community-contributed rules. You can also create rules manually by adding a file like .cursorrules, CLAUDE.md, or .github/copilot-instructions.md to your project root and writing your conventions in Markdown. Our builder handles the formatting and exports to any agent format.

Yes, Agent Rules Builder is completely free. You can generate, customize, and export rules files for any supported AI coding assistant without any cost. There are no usage limits, no sign-up required, and no premium tiers. The tool is built on community rules and is designed to be accessible to all developers. Note: while the service is free to use, it is not open source.

Agent Rules Builder supports all major AI coding assistants: Cursor (.cursorrules), Claude Code (CLAUDE.md), GitHub Copilot (.github/copilot-instructions.md), and Windsurf (.windsurfrules). You can generate rules once and export to multiple formats, so you don't need to maintain separate rule files for each tool. The universal format also works with any AI assistant that accepts text instructions.

No. Agent rules files are only read by the AI coding assistant itself — they are never compiled, bundled, or executed as part of your application. They have zero impact on build times, bundle size, or runtime performance. They're just text files that sit in your project directory.

Yes. You can have .cursorrules, CLAUDE.md, and .github/copilot-instructions.md in the same project without any conflicts. Each tool reads only its own configuration file and ignores the others. Our builder can export to multiple formats from a single set of rules, so you maintain one source of truth.

Yes — committing rules files to Git ensures the whole team benefits from consistent AI behavior. This is one of the biggest advantages of agent rules: every developer on the team gets the same quality AI output. If individual developers have personal preferences (like verbosity or explanation style), use a .local variant added to .gitignore for those overrides.

Agent rules improve code quality in several ways: the AI follows your exact naming conventions and file structure, uses your preferred libraries instead of suggesting random ones, applies your architecture patterns consistently, avoids anti-patterns you've specified, and generates code that passes your linter and type checker on the first try. Teams typically see fewer code review corrections and faster development cycles after adding well-crafted rules.

Aim for under 2,000 words. Most AI context windows handle 4,000–8,000 tokens comfortably, but shorter and more focused rules perform better than long exhaustive ones. Prioritize the highest-impact instructions — your tech stack, critical conventions, and common mistakes to avoid. If your rules grow beyond that, remove redundant or obvious instructions that the AI would likely follow anyway.

Update your rules when you change your tech stack, adopt new conventions, upgrade major dependencies, or notice the AI repeatedly making the same mistake. A good cadence is reviewing the rules file during major dependency upgrades, when onboarding new team members, or quarterly as part of a codebase health check. Treat the rules file like any other configuration — commit changes through PRs and review them with the team.

Ask the AI to generate code that should be directly affected by your rules — for example, "Create a React component" — and verify the output matches your specified conventions for naming, imports, and patterns. If the output doesn't follow your rules, make the relevant instructions more specific, add concrete examples, or move important rules higher in the file where they get more attention from the AI.

Agent rules strongly guide the AI but don't fully override its safety guidelines or core behavior. Think of rules as strong preferences rather than hard constraints — they significantly improve consistency and quality but aren't absolute guarantees. The more specific and clear your instructions are (with concrete examples), the more reliably the AI will follow them.

Markdown is recommended. Headers, lists, and code blocks provide clear structure that helps the AI parse and prioritize your instructions. All major AI coding tools support Markdown in their rules files. Use headers to organize sections (Tech Stack, Conventions, Patterns), bullet points for specific rules, and fenced code blocks for examples of how code should look.

Project rules (like .cursorrules) live in a specific repository and apply only to that project's codebase. Some tools also support user-level or global rules that apply across all your projects — for example, general preferences like code style or explanation verbosity. When both exist, project rules take priority for project-specific conventions, while global rules handle your personal defaults.

Vibe coding is a development approach where you describe what you want in natural language and let AI generate the code. Agent rules supercharge vibe coding by giving the AI deep context about your project — your stack, patterns, and conventions — so the generated code fits seamlessly into your existing codebase instead of producing generic output that needs heavy editing. With good rules, vibe coding produces production-quality code from the start.