If You Build With AI, You Need This File. And The System That Generates It.
AI Rules File Generator: A Beginner-Friendly System for Replit, Cursor, Gemini & Claude Builders.
TL;DR
AI rules files are Markdown instruction files that tell coding agents how to behave inside your project. Tools use different names for them, including AGENTS.md, CLAUDE.md, .cursor/rules/*.mdc, .github/copilot-instructions.md, AI_RULES.md, and replit.md, but the job is the same: give the agent durable project context, conventions, and constraints. The problem is that many public examples teach code style and repo mechanics while skipping product judgment. This guide shows how I use AI rules files as product guardrails for beginner builders, especially around billing, onboarding, data deletion, moderation, notifications, and trust.
Alex McFarland and Michael Spencer are right: creators burn out without systems.
While their post focused on writing, it hits builders just as hard. Without the systems I built, I’d collapse under my own workload.
My standard for this Substack: quality or nothing.
Never AI slop. Never anything I haven’t researched deeply. Nothing I haven’t crawled through myself first.My standard for my builds: ethical, solid, anti-cookie-cutter, and maintainable.
My problem: Time.
Without my obsessively refined workflows, built at the kitchen table while someone asks where their shoes are, I’d have burned out ages ago.
So I build reusable systems as I go. Stubbornly. For writing, for building, for anything that matters.
And one of them sits at the foundation of every vibecoding project I ship: the flow that generates the rules for my AI agents.
Today, I’m sharing that one with you, tuned specifically for beginners who are just starting with coding agents.
Hey, I’m Karo Zieminski 🤗
AI Product Manager and builder.
I write Product with Attitude, an AI newsletter for thousands of subscribers developing critical AI literacy the only way it sticks: through practice.
We don’t just use AI. We build workflows, automations, and products with it, while studying how AI itself is built, positioned, and woven into our work.
If you’re new here, welcome! Here’s what you might have missed:
What’s Inside
What AI Rules Files Are
What AI Rules Files Are
AI rules files are persistent project instruction files for coding agents. They tell the agent what to follow, what to avoid, what to confirm, what commands to run, and which product constraints matter before it edits your code.
You’ll see them under different names: AGENTS.md, CLAUDE.md, .cursor/rules/*.mdc, .github/copilot-instructions.md, AI_RULES.md, .cursorrules, .windsurfrules, replit.md.
Without them, agents improvise. With them, they follow your rules.
Boring product controls are invisible when they work. They become very visible when an agent edits signup, touches billing logic, or removes the moderation check holding the customer experience together.
The Problem With Every AI Rules File I’ve Seen
Since starting with AI-assisted coding, I’ve analyzed a small mountain of repos. Studied how skilled engineers structure their AI files. Read all the guides. Built my own. Buried most of them.
Here’s what I found:
Many respectable AI rules files are technically brilliant.
Too many of them are missing something critical: beginner-friendly product thinking.
They tell the agent to use TypeScript, follow a folder structure, write tests, avoid default exports, and respect whatever architecture pattern the repo already has.
But they rarely tell the agent what product decisions it’s allowed to take.
That’s the dangerous part. A coding agent doesn’t only write code anymore. It makes product decisions while writing code. If the rules file only teaches code style, the agent will optimize the wrong thing with excellent indentation.
Why This Matters Now More Than Before
2025 transformed the industry’s attitude toward AI autonomy: we opened the year warning against autonomous agents, and ended it embracing them because the productivity upside was too big to deny.
The entire tools ecosystem is racing in the same direction:
Replit’s Agent 3, announced two months ago gives builders real, usable autonomy. Duolingo, Zillow, and Coinbase implement it at scale.
(Side note: Replit’s progress this year has been wild. I wouldn’t be surprised if it becomes the viral hit of 2026).Devin is a real autonomous AI software engineer that can plan, code, debug, and deploy in sandboxed environments. Goldman Sachs began deploying hundreds of Devin instances in mid-2025.
Google’s new Antigravity IDE empowers agents to plan, code, and validate software autonomously. Figma, Shopify and Thomson Reuters are among the early adopters.
Whatever tool you’re using, chances are it’s already nudging you toward autonomy, whether you’re ready for it or not.
This changes the role of a rules file: from niche plumbing to the instruction layer for agentic development.
The adoption data points in the same direction.
AGENTS.md describes itself as “a simple, open format for guiding coding agents” and says the format is already used by 60,000+ open-source projects.
Cursor documents project rules through .cursor/rules/*.mdc, including metadata like descriptions, globs, and alwaysApply.
Dyad uses AI_RULES.md to steer its app-building agent.
Why Clear Instructions and Rules Are Critical
Prompt engineering + context engineering + human expertise
= AI agent performance
But the answer is not ‘‘more rules’’.
Bloated rules files can make agents worse. If you paste in 4,000 lines of best practices, you’re likely to get an agent with a lot of context and many ways to misunderstand you.
The useful version is smaller and sharper.
A good AI rules file captures the instructions the agent cannot safely infer from the repo: product decisions, architectural preferences, dangerous actions, required checks, and the human judgment that lives outside the codebase.
A 2026 ETH Zurich study found that LLM-generated context files reduced task success by 3% while increasing cost by 20%.
Human-written files helped only slightly, improving success by 4% while increasing cost by 19%.
The recommendation was wonderfully harsh: skip LLM-generated context files and keep human-written instructions to details the model cannot infer.
Which AI Rules File Does Your Tool Use?
How you set up and manage your rules file depends on which tool you use.
Pro tip: keep one canonical product rules document, then adapt it for the tool you are using. Tool-specific files are adapters.
The source of truth should explain the product clearly enough that Cursor, Claude Code, Replit, Copilot can all inherit the same judgment.
Why I Add Product Thinking
to the Rules For AI
Many vibe coders don’t know the formal conventions or terminology yet, and that’s perfectly fine.
None of us were born knowing what a migration is, let alone an idempotent one. We all start somewhere.
That’s exactly why I tuned this system the way I did: it doesn’t just spit out a file, it walks you through the thinking.
A product-aware rules file teaches the agent the ripple effects behind a feature.
If you add a trial period, your billing logic and onboarding flow change.
If you add notifications, you need scheduling, retries, user preferences, unsubscribe behavior, and failure handling.
If you add user-generated content, you need moderation flows, abuse checks, reporting, and a plan for what happens when someone inevitably uploads garbage.
If you add account deletion, you need data retention rules, audit logs, billing cancellation, email suppression, and a “please do not resurrect this user like a SaaS vampire” policy.
These aren’t coding preferences. They’re product guardrails.
AI rules files for product builders should tell the agent what kind of product we are building, what promises we make to users, and which changes require confirmation before code is touched.
What Should Go Inside an AI Rules File?
Use this filter before adding any rule: can the agent infer this from the repo, or does it need human context?
The tiny test: if removing the rule would cause the agent to make a wrong product decision, keep it. If removing it would only make the agent read two extra files, it may be helpful. If a config, linter, test, or CI check can enforce it, move it there.
What Should Not Go Inside an AI Rules File?
Don’t use your rules file as a junk drawer for every preference you have ever had. That’s how useful context becomes sludge.
Rules files should stay concise, specific, and current. A 200-line instruction file with 15 useful lines makes the agent read 185 lines of noise before it can help you. Worse, your important rules get buried in the middle, exactly where long-context models are most likely to lose attention.
Skip rules the agent can already see: TypeScript, React, Next.js, package managers, formatter choices, and obvious folder names are often visible in config files.
Move enforceable rules into tooling: linting, formatting, naming conventions, test commands, and pre-commit checks belong in linters, formatters, CI, or scripts when possible.
Avoid vague rules: “write clean code,” “use best practices,” and “make it scalable” do not help the agent. They give the model nothing concrete to obey.
Note human judgment in the file: billing promises, data deletion behavior, moderation rules, support expectations, safety boundaries, and “ask before doing this” rules belong here.
The Agent Rules Generator Prompt
Before running the prompt, make sure you’ve read this👇.
How And Why This System Works
The generator asks beginner-product questions before it writes rules. That is the part I care about. The output file matters, but the thinking path matters more because it teaches you which decisions an agent should never guess.
Best practices: It preserves the best of the existing ‘rules for AI’ best practices, with a few additions tailored for beginner-friendly product thinking.
Environment-aware: it generates the right file for your actual setup -
replit.md,cursor.mdc, whatever you’re using.Teaching built in: every rule explains why it exists in simple language. You learn conventions in context, not from a textbook.
Ripple-effect awareness: it shows the downstream consequences beginners without product management experience usually miss:
If you add a trial period ➜ your billing logic + onboarding flow change
If you add notifications ➜ you’ll need scheduling + retries + preferences
If you add user-generated content ➜ you’ll need moderation flows + abuse checks
It also forces a clean Build Now vs. Build Later split so you don’t ship products that look like my Substack drafts folder: 37 features started, none finished.
For agents and for humans: You’ll notice the prompt is written in plain language, not JSON. That’s intentional. It outputs a critical project file so I want to keep it bilingual, readable by both humans and agents.
How To Use The Agent Rules Builder Prompt
This prompt is a full system on its own, so I’m giving it a dedicated page.
Use the prompt in your ChatGPT, Claude, Gemini, or coding-agent setup.
The prompt will feel slower than a one-shot “generate my rules file” command. Good. Fast rules files are how we get confident nonsense with a filename. This one asks enough questions to make the rules useful.
Be prepared to answer questions from the agent - it takes 10 minutes now, saves you hours afterwards.
What To Do With The Generated Rules File
How you set up and manage your rules file depends on which tool you use.
Start by saving the generated file in the format your tool expects. Then do one boring but important pass yourself. Read every rule and ask: would I be comfortable with an agent following this while I’m not watching?
If the answer is no, tighten the rule.
If the rule says “avoid breaking billing,” rewrite it as “before changing billing, list affected Stripe objects, account states, webhooks, trials, and downgrade behavior, then ask for confirmation.”
Agents do better with specific constraints than moral encouragement.
How to Keep Your AI Rules File From Rotting
Agent files aren’t static, they grow with your project. This workflow gives you a rock-solid starting framework, but the real power comes from how you evolve it.
As your projects get deeper, you’ll find yourself adding new instructions, commands, and shortcuts so you don’t have to retype the same prompts over and over.
None of this needs to be perfect on day one, the file will mature alongside your projects.
The post-bug update ritual
Whenever your agent breaks something, don’t just fix it - ask it this:
Based on our session and the fixes we made, what are the key things that should be added to my replit.md to prevent these issues in the future.Here’s a real response from Replit to that exact question, from yesterday’s session:
That gives me a clear to‑do list for updating my project’s replit.md. And, more importantly, I walk away smarter than before.
Every agent failure should harden the system. If the agent broke a payment flow, add a billing guardrail. If it broke auth redirects, add an onboarding rule. If it hallucinated a package, add a dependency rule.
Security: Do Not Paste Random Rules Files
Rules files are powerful because agents obey them. That also makes them an attack surface.
Security researchers have already warned about rules-file backdoors, hidden Unicode, poisoned community templates, and instructions that look harmless to humans while steering the agent toward unsafe behavior.
Before copying a community rules file, treat it like code:
A good rule of thumb: if you wouldn’t paste unknown code into production, don’t paste unknown instructions into the tool that writes your code.
Prompt Versioning
I version the prompt because agent behavior changes, tool conventions change, and my own mistakes keep contributing generously to the research program. Every new weird failure teaches the generator another question to ask.
The version you see today is v9, and I’ll be updating it in real-time on the prompt page so you always have access to the latest iteration.
Key Takeways
Rules files are how you “raise” well‑behaved AI agents: they encode conventions, guardrails, and product context instead of letting agents improvise.
AI rules files should be treated as product guardrails, not only code-style checklists.
Most public rules files miss beginner‑friendly product thinking, so agents ship changes that ignore flows like billing, onboarding, and moderation.
Minimal rules beat bloated rules. Keep what the agent cannot infer, move enforceable rules into tooling, and delete the obvious stuff.
The Agent Rules Generator is a single prompt that outputs platform‑specific rules files (Replit, Cursor, Gemini, Lovable, Antigravity) with explanations in plain language.
You use it as a living system: generate an initial rules file, then update it every time an agent breaks something so each failure hardens the rules.
As autonomous coding agents go mainstream, builders who invest in strong rules, specs, and governance prompts will get the upside without letting agents quietly wreck their product.
Everything I’ve managed to build this year, every workflow, post, and bug fix squeezed between “Moooom!” and a Teams meeting, only happened because I had systems to hold the weight.
This is one of those systems.
AI rules files are not glamorous, but they’re useful.
Use the rules file to teach the agent your product judgment. Keep it small. Keep it current. Add the guardrails a beginner would not know to name yet.
I hope this one helps you too.
If you try the Agent Rules Generator, I want to see what it catches in your project. Especially the weird product ripple effects. Those are the ones that teach all of us faster.
Additional Resources
In this article, I make the case for context engineering as the real system behind reliable AI products, then hand you the framework, Skill, and prompt I use to make it practical.
Claude Skills Library
I maintain two tested Claude Skills libraries: one with skills I’ve built myself, and one with skills shared by the Product with Attitude community.
You can find the community-built Claude Skills in Attitude Vault. Built one yourself? Share it there too.
FAQ
What is an AI rules file?
An AI rules file is a persistent instruction file for a coding agent. It usually lives in or near your project repository and tells the agent what conventions, commands, constraints, and product guardrails to follow while changing code.
What is the difference between AGENTS.md, CLAUDE.md, and Cursor rules?
They are different file formats for the same broad job: giving coding agents persistent project instructions. AGENTS.md is an open format for agent guidance, CLAUDE.md is commonly used with Claude Code, and Cursor uses .cursor/rules/*.mdc files that can be scoped by metadata and file patterns.
Should beginners use AI rules files?
Yes, and the file is concise and product-aware. Beginners benefit because a rules file makes hidden judgment visible. The trap is generating a giant generic file full of rules the agent can already infer.
Can AI rules files make coding agents worse?
Yes. Bloated or generic rules files can waste context, increase cost, and distract the agent from useful project information. The safest pattern is to include only non-inferable instructions, product guardrails, and rules that change agent behavior.
Are AI rules files a security risk?
They can be. A copied rules file can include unsafe instructions, hidden Unicode, or malicious guidance. Treat community rules files like code: review them before use, never include secrets, and delete anything you cannot explain.
What is CLAUDE.md and why does it matter?
CLAUDE.md is a project instruction file used with Claude Code to define conventions, context, guardrails, commands, and expectations. It matters because agentic coding gets much better when Claude does not need to rediscover your project rules every session.
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Great resource! I know from personal experience how important it is to do the product thinking up front and give coding agents well-documented standards…and what happens when you don’t! 😆
I did this for my most recent project and have experienced so much less chaos…
same approach I started. a command file that clearly guide the llm, only upload the custom input for each new work. so value your post, I will adjust and improve my strategy with it. thanks again @Karo