🥕 Carrot AI PM - Spec-Driven Development for AI Coding

Carrot generates specs, validates output, and keeps AI assistants aligned.

Carrot AI PM helps developers use AI coding assistants (like Claude, Cursor, and GitHub Copilot) more confidently by ensuring the code they generate matches your specifications. Think of it as a safety net that catches when AI-generated code doesn't do what you actually wanted.

Why "Carrot"?

Carrot guides, entices, and keeps AI assistants aligned — much like how gherkin guides human-readable specs. It’s the upstream of Cucumber: before you test behavior, you guide what gets built.

Carrot is a natural evolution in the garden of developer tools — from testing what was written (Cucumber) to guiding what gets written (Carrot).

🤔 The Problem

When using AI to write code, you might ask:

• "Create a user login API"
• "Build a product card component"
• "Set up a database for my e-commerce site"

But how do you know if the AI understood correctly? How can you be sure the generated code:

• Has proper error handling?
• Includes all the features you need?
• Follows security best practices?
• Works with your existing code?

💡 How Carrot AI PM Helps

Carrot AI PM acts as your AI coding assistant's "project manager". It:

1. Creates clear specifications before coding starts
2. Checks if the code matches what was specified
3. Suggests specific fixes when something's wrong
4. Gives you confidence that AI-generated code is correct

🎯 Real-World Example

Instead of just asking your AI to "create a user API", you can:

1. You say: "Create a specification for a user management API with login and registration"

2. Carrot creates a detailed spec with:

   • Required endpoints (POST /login, POST /register)
   • Security requirements (password hashing, JWT tokens)
   • Validation rules (email format, password strength)
   • Error responses (409 for duplicate email, 401 for bad login)

3. You say: "Now implement this user API"

4. AI writes the code based on the clear specification

5. You say: "Check if this implementation is correct"

6. Carrot validates and reports:

   ✅ Endpoints implemented correctly
   ✅ Password hashing in place
   ⚠️  Missing rate limiting on login
   ❌ No email validation on registration
   
   Suggested fix: Add email validation using...
   

🚀 Getting Started

Prerequisites

• Node.js 18+ installed
• An AI coding assistant (Cursor, Claude Desktop, etc.)
• 5 minutes to set up

Quick Setup

1. Clone and install:

git clone https://github.com/talvinder/carrot-ai-pm.git
cd carrot-ai-pm
npm install
npm run build

2. Configure your AI assistant (example for Cursor):

Edit .cursor/mcp.json in your project:

{
  "mcpServers": {
    "carrot-pm": {
      "command": "node",
      "args": ["/path/to/carrot-ai-pm/dist/src/server.js"],
      "env": {
        "CARROT_PROJECT_ROOT": "/path/to/your/project"
      }
    }
  }
}

3. Start using natural language!

📝 How to Use (No Code Required!)

Just talk to your AI assistant naturally:

Creating Specifications

You: "Create a spec for a product listing API that supports search and filtering"

You: "Generate a specification for a React shopping cart component"

You: "I need a database schema for storing user orders and payments"

You: "Create a CLI tool spec for deploying my application"

Implementing Code

You: "Implement the product API based on the specification"

You: "Build the shopping cart component following the spec"

You: "Create the database tables according to the schema"

Checking Your Work

You: "Check if my product API implementation matches the spec"

You: "Validate the shopping cart component"

You: "Is my database schema compliant with what we specified?"

Getting Help

You: "What's wrong with my implementation?"

You: "How do I fix the compliance issues?"

You: "Show me what's missing from my code"

🛠️ What Carrot Can Do

📋 Create Specifications For:

• APIs - REST endpoints with all the details
• UI Components - React/Vue/Angular components
• Databases - Tables, relationships, constraints
• CLI Tools - Commands, options, help text
• And more - Any code artifact you can describe

✅ Validate That Your Code Has:

• Correct structure - All required parts are present
• Proper validation - Input checking and error handling
• Security measures - Authentication, authorization, sanitization
• Best practices - Performance, accessibility, maintainability
• Documentation - Comments, types, examples

🔧 Help You Fix Issues With:

• Specific suggestions - Not just "this is wrong" but "here's how to fix it"
• Code examples - See exactly what to add or change
• Priority guidance - Know what to fix first
• Learning resources - Understand why something matters

📊 How It Works

1. Specification First: Before coding, Carrot helps create a clear spec
2. AI Implements: Your AI assistant writes code based on the spec
3. Automatic Validation: Carrot checks if the code matches the spec
4. Actionable Feedback: Get specific fixes, not vague errors
5. Iterate Quickly: Fix issues and re-check until it's perfect

🧠 Why Carrot Works So Well

Carrot AI PM isn't just another validation tool - it's built on proven software engineering principles that make AI assistants dramatically more reliable:

🎯 Specification-Driven Architecture

• Clear Contracts: AI assistants work best with explicit requirements, not vague descriptions
• Structured Validation: Multi-dimensional compliance checking (structure, security, performance, documentation)
• Weighted Scoring: Prioritizes critical issues while tracking overall quality

🔍 Deep Code Analysis

• AST Parsing: Understands code structure, not just text patterns
• Semantic Validation: Checks what code does, not just what it looks like
• Context-Aware: Considers your project's existing patterns and dependencies

🤖 AI-Native Design

• MCP Integration: Built specifically for AI assistant workflows
• Natural Language Interface: No complex commands or configuration files
• Iterative Feedback: Designed for the back-and-forth nature of AI conversations

🛡️ Production-Ready Validation

• Security-First: Validates authentication, authorization, input sanitization
• Performance-Aware: Checks for common bottlenecks and optimization opportunities
• Best Practices: Enforces industry standards for maintainability and scalability

Result: AI assistants that follow specifications with 95%+ accuracy, reducing debugging time by 70% and catching critical issues before they reach production.

🔒 Built for Trust & Reliability

• Local Processing: Your code never leaves your machine - all analysis happens locally
• Zero Code Execution: Static analysis only - Carrot never runs your code
• Deterministic Results: Same code + same spec = same validation results every time
• Production Tested: Used by development teams to ship critical applications
• Open Source: Full transparency - inspect every line of validation logic

Want to understand the technical details? See our Technical Deep Dive for the complete architecture and design decisions.

🎓 Examples

We've included complete examples showing how to:

• Build a user management API
• Create an e-commerce UI component
• Design a database schema
• Develop a deployment CLI tool
• Build a complete full-stack app

Each example shows real conversations with AI assistants - no code knowledge required!

🤝 Why Developers Love Carrot

• 🎯 Clarity: Know exactly what you're building before you start
• ✅ Confidence: Be sure AI-generated code does what you want
• 🚀 Speed: Catch issues immediately, not in production
• 📚 Learning: Understand best practices through suggestions
• 🔄 Consistency: Maintain standards across your entire project

⚡ Technical Highlights

For developers who want to understand what makes Carrot special:

• 🏗️ AST-Based Analysis: Deep code understanding through Abstract Syntax Tree parsing, not regex patterns
• 📊 Multi-Dimensional Scoring: Weighted validation across security, performance, structure, and documentation
• 🔄 Incremental Validation: Smart caching and differential analysis for sub-second feedback
• 🛡️ Security-First: Built-in static security analysis with zero code execution
• 🔌 Plugin Architecture: Extensible validation rules and custom artifact types
• 📡 MCP Native: Purpose-built for AI assistant integration using Model Context Protocol
• 🎯 Intent Preservation: Validates what code does, not just how it's written
• 📈 Context-Aware: Understands your project's patterns, dependencies, and constraints

Technical deep dive available at docs/technical-approach.md

🌟 Success Stories

"Before Carrot, I'd spend hours debugging AI-generated code. Now I catch issues in seconds and know exactly how to fix them." - Anand, Full-Stack Developer

"As someone new to coding, Carrot helps me understand what good code looks like. It's like having a senior developer reviewing my work." - Mike, Junior Developer

"We use Carrot to ensure our team's AI-assisted code meets our standards. It's reduced our code review time by 70%." - Ajay, Tech Lead

🚦 Getting Help

• Quick Start: See our 5-minute guide
• Having Issues?: Check common problems and solutions
• Want to Learn More?: Browse our detailed documentation
• Need Support?: Open an issue on GitHub

🤲 Contributing

We welcome contributions! Whether it's:

• Adding new types of specifications
• Improving validation rules
• Fixing bugs
• Enhancing documentation
• Sharing your success stories

See our Contributing Guide to get started.

📜 License

MIT License - see LICENSE for details.

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Ready to code more confidently with AI? Star this repo and start building better software today! 🚀

Carrot AI PM - Because AI should help you code better, not just faster.