Based on a tutorial by AI Jason
If you’ve been using Cursor for coding, you’ve probably experienced that frustrating moment when you ask for a small change and it completely breaks your entire project. Or worse, Cursor implements something without understanding your dependencies, leading to a cascade of errors.
This is a common problem with AI coding agents, but there’s a game-changing technique that can dramatically reduce these errors: implementing a task management system for your AI coding workflow.
Quick Navigation
- The Core Problem with AI Coding Agents
- Basic Task Management Workflow
- Advanced Tools: Taskmaster AI & Boomerang Task
- Roo.Coder’s Boomerang Task Feature
- Setting Up Taskmaster AI with Cursor
- Real-World Implementation Demo
- Results & Best Practices
The Core Problem with AI Coding Agents
AI coding agents like Cursor often struggle with context management and dependency awareness. When you ask for a change, they might implement something that breaks existing functionality or requires dependencies that haven’t been set up yet.
Common Issues:
- AI agents mess up entire projects when making small changes
- Lack of awareness about project dependencies
- Implementation order problems causing cascading errors
- Limited context about overall project structure
My Take:
The solution isn’t to abandon AI coding tools, but to give them better structure and context. Task management systems act like a roadmap for your AI agent, preventing it from getting lost in complex projects.
Basic Task Management Workflow
Before diving into advanced tools, let’s understand the core concept. At its foundation, task management for AI coding means breaking down complex requirements into smaller, manageable tasks that your AI agent can track and execute systematically.
Basic Implementation Steps:
- Create a cursor rule that references a task.md file
- Ask Cursor to break down your project into small tasks
- Have Cursor mark tasks as completed as it works
- Maintain context of the overall implementation plan
# Example Cursor Rule
Always refer to task.md to keep track of completed and pending tasks.
Mark tasks as done when completed and update progress regularly.
This basic approach already provides significant improvements, but advanced tools take it much further.
Advanced Tools: Taskmaster AI & Boomerang Task
While basic task management helps, specialized tools like Taskmaster AI and Boomerang Task bring sophisticated task management directly into your AI coding workflow.
Taskmaster AI Features:
- Breaks down PRDs into logical, dependency-aware tasks
- Uses Claude 3.7 or advanced models for intelligent task creation
- Analyzes task complexity and suggests further breakdown
- Integrates deeply with Cursor and Windsurf
- Provides command-line tools for task management
Boomerang Task Benefits:
- Gives AI agents dedicated task management tools
- Enables progress tracking across complex projects
- Works within existing AI coding environments
- Provides structured approach to project breakdown
Roo.Coder’s Boomerang Task Feature
Roo.Coder offers an open-source alternative to Cursor that lives inside Visual Studio Code. What makes it special is its customizable agent modes, including the Boomerang Task mode for project planning.
Roo.Coder Advantages:
- Completely free to use (just provide your API key)
- Custom agent modes for different tasks
- Built-in architect, coding, and debug agents
- Token consumption and cost tracking
- Automated testing capabilities
My Take:
Roo.Coder’s specialized agents work like having different experts for different phases of development. The architect agent handles planning, while the coding agent focuses on implementation – each with the right context for their role.
Setting Up Taskmaster AI with Cursor
Let’s walk through the complete setup process for Taskmaster AI, which integrates seamlessly with Cursor and Windsurf.
Installation Steps:
- Install via npm:
npm install -g taskmaster-ai - Set up your project structure first
- Run
taskmaster initin your project folder - Configure API keys for Anthropic and Perplexity
- Create your PRD (Product Requirements Document)
# Create a new Next.js project
npx create-next-app@latest my-app --typescript --tailwind --eslint
# Navigate to project
cd my-app
cursor .
# Initialize Taskmaster
taskmaster init
Key Configuration Files:
- .cursorrules – Generic rules for Cursor behavior
- step-workflow – Commands for task management
- .env.example – API key configuration template
- scripts/prd.txt – Your project requirements document
The setup creates a structured environment where Cursor understands how to work with tasks, maintain context, and even create new rules based on mistakes it encounters.
Real-World Implementation Demo
AI Jason demonstrated this workflow by building a fully functional multiplayer drawing game similar to Scribble, where players draw words and GPT-4 judges the results. The entire implementation was done in one session with minimal errors.
Project Breakdown Process:
- Create PRD with help from Cursor or specialized tools
- Run
taskmaster parse prd scripts/prd.txt - Review generated tasks with
taskmaster list - Analyze complexity with
taskmaster analyze complexity - Break down complex tasks further as needed
# Parse PRD into tasks
taskmaster parse prd scripts/prd.txt
# List all tasks with dependencies
taskmaster list
# Analyze task complexity
taskmaster analyze complexity
# View complexity report
taskmaster complexity report
# List with subtasks
taskmaster list --subtasks
Implementation Features Achieved:
- User authentication and lobby system
- Multiplayer room creation and joining
- HTML5 canvas drawing functionality
- Real-time timer and game state management
- GPT-4 integration for image evaluation
- Scoring and winner selection system
Results & Best Practices
The results speak for themselves: a fully functional multiplayer game built in approximately 20 minutes with minimal errors. This represents a massive improvement over traditional AI coding approaches.
Key Success Factors:
- Dependency Mapping: Tasks are ordered logically with clear dependencies
- Complexity Analysis: High-complexity tasks get broken down further
- Context Management: Each task runs with appropriate context
- Progress Tracking: Clear visibility into what’s done and what’s next
- Error Reduction: Structured approach prevents common mistakes
My Take:
The key insight here is that AI coding agents work best when they have structure and context. Instead of asking them to understand everything at once, we give them a clear roadmap to follow. It’s like the difference between giving someone vague directions versus a detailed GPS route.
Pro Tips for Implementation:
- Start with detailed PRDs – the better your requirements, the better your tasks
- Don’t skip complexity analysis – it catches potential issues early
- Use “YOLO mode” in Cursor for hands-off implementation
- Switch to Gemini 2.5 Pro to avoid token limits
- Let Cursor create new rules based on errors it encounters
This workflow represents just the beginning of sophisticated AI-assisted development. As these tools continue to evolve, we can expect even more impressive results with less manual intervention.
