AI Agent Rule Files Chaos

Fragmented State of AI Agent Rule Files

The AI coding landscape is experiencing a fundamental transformation as developers increasingly rely on AI assistants to enhance productivity and code quality. But beneath this exciting revolution lies a messy problem that has become more apparent the deeper I’ve gone down the rabbit hole:

The ecosystem of rule files that guide these agents is totally fragmented.

From .cursorrules to .clinerules, from global_rules.md to AGENT.md, every tool has its own flavor of configuration—and none of them talk to each other. So I decided to do a deep dive into the state of prompting and prompt files across this rapidly evolving ecosystem. Here’s what I found.

The Fragmented Landscape of AI Coding Rules

Desktop IDE Tools: The Rule File Explosion

Cursor pioneered the rules file movement with .cursorrules, but they’ve since evolved to a more structured format using .cursor/rules/*.mdc—Markdown files with YAML frontmatter and XML-like tags. This newer setup supports multiple activation modes (Always, Auto Attached, Agent Requested, Manual), allowing developers to design more nuanced, context-aware configurations.

Windsurf uses a dual structure: global_rules.md for workspace-wide instructions and .windsurf/rules for project-specific rules. It’s a flexible system but has seen reports of rule conflicts and recognition issues.

Cline transitioned from a flat .clinerules file to a .clinerules/ folder structure with modular markdown files, giving teams more control and organization options.

GitHub Copilot relies on .github/copilot-instructions.md, enabling team-wide rules and customization via GitHub’s version control ecosystem.

VS Code introduced .prompt.md files—reusable, referenceable prompt snippets. Think of them as your personal prompt library inside your IDE.

Online Platforms: The Prompting Revolution

v0 (by Vercel) is fully system-prompt driven. It relies on tightly structured system messages to generate UI code based on best practices and strict conventions.

Lovable is obsessed with prompt design. They’ve built extensive systems to support few-shot prompting, meta-prompting, and beginner-to-expert guidance.

Replit Agent focuses on natural language prompting and interaction design. Features like "Improve Prompt" and inline image attachments make it intuitive—but until mid-2025 had no structured prompt file. Now Replit supports a .replit.md file for project-specific agent instructions.

Claude Code: A Comprehensive Approach to AI Agent Configuration

Claude Code flips the IDE-extension model by operating as a CLI-based AI assistant (Integrating with VS Code, Cursor, JetBrains, but independently).

Settings Hierarchy

~/.claude.json – global config (including MCP servers)
~/.claude/settings.json – user preferences
.claude/settings.json – project-shared config
.claude/settings.local.json – local user overrides

CLAUDE.md Memory System

./CLAUDE.md – project memory
~/.claude/CLAUDE.md – global user memory
Recursive discovery & @import for modular context (up to 5 levels)

Custom Commands & Hooks

Slash commands in .claude/commands/, using $ARGUMENTS
Hooks: PreToolUse, PostToolUse, Notification, Stop

System Prompt Integration

Persistent system prompts across sessions; /add-dir to dynamically extend context.

MCP Integration

Full support for Model Context Protocol—Claude can act as both client/server to tools and APIs.

VS Code GitHub Copilot: Custom Chat Modes (`.chatmode.md`)

VS Code offers Ask, Edit, and Agent modes by default. Developers can now define custom chat modes using .chatmode.md files.

How It Works:

Run Chat: New Mode File in Command Palette
Choose workspace or user mode file location
Name the chat mode
Edit the generated .chatmode.md with:
- Front Matter: description, tools (list), and optional model
- Body: Instructions for the mode

By default, mode files live in .github/chatmodes/, configurable via chat.modeFilesLocations setting.

Example: Beast Mode 3.1 (by Burke Holland, Microsoft)

---
description: Beast Mode 3.1
tools: ['changes', 'codebase', 'editFiles', … 'vscodeAPI']
---

# Beast Mode 3.1

You are an agent—keep going until the user’s query is completely resolved… avoid unnecessary verbosity but be thorough.

Beast Mode 3.1 layers an opinionated, developer‑style workflow onto agent mode for GPT‑4.1 and is one of the most popular community-defined chat modes.
References: Burke Holland’s Beast Mode blog post (July 2025), GitHub gist, and community spread via “Awesome Copilot Chat Modes” collection on GitHub.
Some developers have noted a bug: specifying tools in front matter may disable tool usage—workarounds suggest removing the tools array and using VS Code's UI to pick tools.
Sources: VS Code chat modes spec, Medium blog, Reddit discussions.

Cursor’s `.mdc` Rule Files: A Structured Format with XML-Like Tags

Cursor uses a special file extension .mdc (Markdown Cursor) to differentiate rule files from regular Markdown. These reside in the .cursor/rules/ directory and are automatically included in AI context based on metadata.

Anatomy of a `.mdc` Rule

YAML Frontmatter

---
description: ACTION TRIGGER OUTCOME format
globs:
  - "*.ts"
alwaysApply: false
tags: [style, naming]
priority: 1
version: "1.0.0"
---

Pseudo‑XML Body

<rule>
  <meta>
    <title>Enforce camelCase variable names</title>
    …
  </meta>
  <requirements>
    <non-negotiable>
      <description>Never use snake_case variables</description>
      <examples>
        <correct-example><![CDATA[const userName = 'Joe';]]></correct-example>
        <incorrect-example><![CDATA[const user_name = 'Joe';]]></incorrect-example>
      </examples>
    </non-negotiable>
  </requirements>
</rule>

Tags like <requirements>, <examples>, and <references> impart semantic precision and make rules more machine-readable for LLMs. File-naming conventions often include numeric prefixes (001-style.mdc, 200-testing.mdc) to organize rule sets.
Reference: Cursor Rules documentation

Emerging Standardization Efforts

AGENT.md

Sourcegraph’s AGENT.md spec proposes a universal, hierarchical Markdown-based file for project guidance: build scripts, styles, workflows, imports, etc. Designed to replace tool-specific configs with a shared standard. more info

MCP (Model Context Protocol)

A vendor-agnostic communication protocol enabling agents to call external tools, APIs, or fetch data at runtime. Backed by OpenAI, Google, AWS.

Agent File Format (`.af`)

From Letta AI—a single-file snapshot of agent memory, tools, config, and conversation state for portability and interoperability. more info

llms.txt

A proposal inspired by robots.txt, where websites host an llms.txt file listing key Markdown docs for AI agents to read and index. more info

Open Standards & Observability

OASF (Open Agentic Schema Framework) within AGNTCY defines standard agent capabilities schema, more info
OpenTelemetry GenAI conventions bring logging/tracing structure to prompt evaluation and tool invocation more info

Developer Quick Reference Table

Tool/Platform	File Location	Format	Notes
Cursor	`.cursor/rules/`	`*.mdc` (YAML + XML-like body)	Structured activation/rule definition format
Windsurf	`/`, `.windsurf/rules/`	`global_rules.md`, `*.md`	Dual-level config (workspace + project)
Cline	`.clinerules/`	`*.md`	Modular, Markdown-based rule files
Copilot Instructions	`.github/`	`copilot-instructions.md`	Custom instructions applied to chat/codegen
VS Code Prompt Files	`.github/prompts/`	`*.prompt.md`	Reusable prompt templates in editor chat
VS Code Chat Modes	`.github/chatmodes/`	`*.chatmode.md`	Persona-based chat behavior (tools + instructions)
Claude Code	project + home	`CLAUDE.md`, `*.json`, `.commands/`	Memory files + tool config + slash commands
Replit Agent	project root	`.replit.md`	Project-specific instructions (added mid 2025)
v0 (Vercel)	system-only	(no user-visible file)	UI-code generation via system prompts
Lovable	system-only	(no user file)	Internal prompt design workflows
Letta AI	n/a	`.af`	Portable agent snapshot format

The Value of Standardization

Productivity: Avoid repeating instructions across tools
Security: Single source of truth reduces misconfig
Collaboration: Unified onboarding via shared rule files
Interoperability: Agents can interpret the same config across platforms

The AI agent market is projected to grow from $5.1 B in 2024 to $47.1 B by 2030. Standardization isn’t just good—it’s inevitable.

Final Thoughts: Let’s Build the Foundation

Rule files aren’t just config—they’re a protocol layer between humans and AI coding agents. Right now, there’s too many dialects. The ecosystem is just beginning to coalesce around shared formats like AGENT.md, .chatmode.md, .mdc, and llms.txt. For agent capabilities and traceability, platform-neutral protocols like MCP and schemas like OASF are gaining traction.

My advice: start with one tool you use, write clean, shared rule files for your project—then keep an eye on emerging standards. When people begin converging around interoperable formats, you’ll already be ready to migrate easily.

Let’s stop writing the same prompt ten different ways—and start writing the next chapter in human–AI software creation.

– Joe

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Sam Moore2 days ago

Totally feeling this. I've run into the exact same mess—every tool has its own format, its own rules, its own assumptions. What we really need is a dead simple, pragmatic standard that makes configuration interoperable across platforms. Something developers can adopt without reinventing the wheel every time. Appreciate you surfacing this.