Cursor Rules Generator: Turn Project Facts Into Useful Rules

A Cursor rules generator takes a description of your project and produces the .cursorrules file or .cursor/rules/ entries that guide Cursor's AI assistant. The output is only as good as the input: rules drawn from a generic description drift away from your actual code, while rules drawn from real commands, conventions, and decisions stay useful.

Tools like fenbs keep your approved project decisions in one place outside any single chat, so you can pull the right facts into a generated rules file rather than reconstructing them from memory. That keeps your rules file short, accurate, and easy to update.

Why generated rules often fall short

Most generated Cursor rules fail in three predictable ways. They repeat framework defaults that every developer already knows. They omit the one build command your project runs differently from the tutorial. And they describe a file structure that existed six months ago. A better generator cannot fix bad source material.

Before you run any generator, collect the facts that your project actually depends on. These are the inputs that separate a useful rule from a copied template.

• The actual build, test, and lint commands from your package.json, Makefile, or CI config.
• The file layout that differs from the framework default, including any monorepo structure.
• Naming conventions your team has agreed on: casing, import order, preferred libraries.
• Files, folders, or services an assistant must not touch or push without permission.
• Any hard constraints on file size, test coverage, or adding dependencies.

Two approaches to generation

The first approach is to ask an AI assistant directly. Paste your package.json, a short description of the project structure, and the conventions you care about, then ask it to produce a .cursorrules file. The second is to use a dedicated generator tool that prompts you through a series of questions and produces structured output.

Both work. The assistant path gives you more control over which facts go in. The tool path is faster for a standard stack. Either way, the result needs a review pass before it goes into version control. See Cursor rules examples for patterns that make good starting points.

A prompt structure that produces specific output

When asking an AI to generate your rules, a structured prompt produces more usable output than a paragraph description. Give it the actual commands and paths rather than asking it to guess.

Project: [name and one-line purpose]
Stack: [language, framework, runtime version]
Build command: [exact command]
Test command: [exact command]
Lint/format: [exact command]
File layout: [differences from the framework default]
Conventions: [naming, import order, anything team-specific]
Off-limits: [files, folders, or services the AI must not change]
Generate a .cursorrules file from the above.

Reviewing the output before committing

Read the output before you commit it. Generated rules contain three kinds of noise: facts that are wrong for your project, defaults too obvious to be useful, and instructions the generator invented without any basis in your source material. Remove all three.

Check each rule against your actual repository. Open package.json and verify the commands. Look at the directory tree and confirm the paths. If a rule mentions a library, check it is in your dependencies. A rule that fails this check misleads an assistant that would otherwise ask. See Cursor rules for AI projects for how to fit rule maintenance into a regular project rhythm.

• Every command in the rules should match a script or binary that is actually present.
• File paths should reflect the current layout, not an older version or a tutorial default.
• Constraints on off-limits files should name the actual paths, not generic categories.
• Style rules should align with what your linter enforces automatically.

Where decisions belong: rules file versus fenbs

Not every decision fits in a rules file. A rules file captures stable conventions that apply to every session: what to run, what to avoid, how to name things. It does not capture the reasoning behind a convention, a choice still under discussion, or a decision that changes with the current task.

Those belong in fenbs. Decisions and rules in fenbs explain why a convention was chosen, record when it changed, and let assistants read the current state without opening a chat from three months ago. When you regenerate rules, pull the relevant Decisions from fenbs rather than reinventing them from scratch.

How to generate Cursor rules with fenbs

1. Create a free account and add your project to fenbs. Write the key conventions as Decisions and rules, including approved commands and any off-limits files or folders.
2. Connect Cursor to fenbs over MCP from Integrations. Once connected, ask Cursor to read the project context before generating rules.
3. Ask the assistant to draft a .cursorrules file based on the project Decisions. It will use the approved facts rather than guessing from a generic description.
4. Review the output against your actual repository. Correct anything that does not match and update the relevant Decision in fenbs to reflect what changed.
5. Commit the rules file. The reasoning behind each rule lives in fenbs, where the next session can find it without replaying the conversation.

Keeping rules current over time

Rules files go stale when the project changes and nobody updates them. Add a rules review to your workflow when you rename a script, restructure folders, or change a tool. A quick comparison between the rules file and the current package.json takes less than a minute and prevents an assistant from running a command that no longer exists.

When you update a convention in fenbs, ask a connected assistant to regenerate the affected rules from the updated context. The ai-code-generators guide covers how AI coding tools use project context more broadly.