ARO: Agent Readability Optimizer

"SEO for your code, optimized for AI Agents."

ARO is a CLI toolkit designed to optimize your codebase for AI agents (Cursor, Windsurf, Devin). It eliminates the Hallucination Tax and ensures your code is understood by AI "instantly".

⚡ Quick Start

Analyze any project instantly without installation:

npx @agent-aro/cli audit
# or if installed globally
aro audit

📦 Installation

For frequent use, install ARO globally:

npm install -g @agent-aro/cli

🛠️ Usage

Audit & Analysis

Deeply analyze your project structure and calculate AI financial debt.

aro audit          # Standard audit
aro audit --silent # Output only the score (for CI/CD)

Intelligent Refactoring

Analyze large files and get smart splitting suggestions. Optionally apply them automatically.

aro refactor          # Analyze and generate refactoring plan
aro refactor --apply  # Auto-split classes and extract types (agentic mode)

Rule Generation

Generate optimized configuration files for specific AI editors.

aro rules          # Generates .cursorrules, .windsurfrules, etc.

Badge Generation

Generate an ARO Score badge and automatically patch your README.md.

aro badge --update

CI/CD Automation

Initialize a GitHub Action to automatically audit your project on every push/PR.

aro init-ci

MCP Server

Enable live integration so AI Agents can query your project structure directly.

aro mcp            # Starts the MCP server

💡 Tip: All commands work with npx @agent-aro/cli if you haven't installed globally.

🔌 Local LLMs: ARO's MCP server works with local LLMs like Ollama and LM Studio. See examples/mcp-local-llm for setup instructions.

Key Features

• 🎯 Real-time ARO Score: Get a deterministic 0-100 rating of your code's AI-readiness.
• 🤖 Agentic Refactoring: Auto-split large files with --apply flag for instant optimization.
• 📊 Context File Analysis: Scores AI instruction files (AGENTS.md, .cursorrules) for quality.
• 💰 Financial Analyzer: Calculate the annual "AI-Debt" in USD and wasted developer hours.
• 🛰️ MCP Server: Native integration for AI Agents to query your structure directly.
• 🛡️ Security Gate: Integrated security checks for keys and dangerous functions.

Scoring Calculation

The score (0-100) is based on AI-Agent understanding efficiency:

1. Documentation (25pts): README quality and completeness.
2. Structure (20pts): Organized directories and entry points.
3. File Size (30pts): Avoiding truncation with manageable file sizes.
4. AI Context (25pts): Quality of agent instruction files (AGENTS.md, .cursorrules).

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Contributing

We welcome all PRs! Check our CONTRIBUTING.md to get started.

MIT (c) Hasan Kemal Demirci