Invest in Cursor Rules: A Four-Level Maturity Framework

Guides
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1

After a year of AI-assisted development, I’ve found that the biggest productivity differentiator isn’t the model you use. It’s the quality and structure of your rules.

Most developers skip rules entirely. They prompt, hope the output is usable, fix what comes back. Each session starts from zero.

The consequence: fragmented codebases, architecture decisions left to the AI, inconsistent patterns.

The Four Maturity Levels

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Level Approach Result
1 No rules. Pure conversation. Most common. Inconsistent output.
2 Architecture docs as context Written for humans, not machines
3 AGENTS.md files Better, but too high-level
4 Capability-specific rules with code examples This is where productivity jumps

What Level 4 Looks Like

Think in terms of capabilities. In a full-stack web app: authentication patterns, API design conventions, database access layers, state management, form validation, error handling, UI components. Each gets its own rule file with explicit instructions and working code examples.

You also need generic rules that apply everywhere: language conventions, framework patterns, project structure, code style. These form the foundation. Capability rules build on top.

Pro Tips

  1. Use globs and alwaysApply to control when rules load. Database rules only when touching data layer files. UI rules only for frontend work. This saves context window for what matters right now.

  2. Create a feedback loop. When the AI makes a mistake, treat it as a trigger. Improve your rules so it doesn’t happen again.

  3. Rules don’t replace documentation. You still need docs for humans. Rules translate those human decisions into machine-readable instructions.

Resources

How Cursor can help by working together with the developer
Agent Ignoring Rules Set in Cursor Settings
3

Every time I added code samples to a task or included them in any form in TODO.md, the agent performed worse than without them.

This might help weaker models, but for strong ones, it’s better to write only directives; they’ll automatically collect code samples from the repository as they gather context.

4

Interesting observation. I’ve had the opposite experience, but let me think through why results might differ.

My hypothesis: it depends on codebase consistency.

If your codebase has clean, consistent patterns, the AI finds good examples on its own. Directives point it in the right direction, and the existing code does the rest. Adding explicit examples is redundant context.

If your codebase has multiple patterns for the same thing (legacy approaches, experimental code, different styles from different contributors), the AI has to choose. Code examples in rules define which pattern is authoritative. Directives alone leave that choice to the model.
I work on projects where the same capability is implemented three different ways across the codebase. Without explicit examples, the AI picks one. Sometimes the wrong one. Examples fix that.

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5

my 2cents

Use attachfile in rules. Keep the actual rules content in .md in skills folder and you are claude code ready at the same time :))

Plus you can use it as context for agents SDKs.

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