How to Use Claude Task Master | Auto-Generate Tasks from PRD in Cursor

What Is Claude Task Master?

Claude Task Master is an open-source task management system published on GitHub as eyaltoledano/claude-task-master. It integrates via MCP (Model Context Protocol) with major AI coding environments including Cursor, Windsurf, VS Code, Lovable, and Roo, and supports multiple AI models such as Claude (Anthropic), OpenAI, and Google Gemini.

The core idea is simple: by establishing a workflow where "you write large requirements as a PRD and the AI breaks them down into small tasks," it structurally compensates for the AI's weakness of "getting confused when trying to solve problems that are too large all at once."

According to tessl.io's explanation, there are reports that introducing this tool has reduced coding errors by up to 90%.

3 Problems Claude Task Master Solves

Problem 1: Context Management Across Sessions

AI coding tools struggle to maintain context across conversation sessions. Claude Task Master records tasks and progress in tasks/tasks.json at the project root, allowing the AI to maintain awareness of "how far along the project is" even across multiple sessions.

Problem 2: Decomposing Compound Requirements

When you try to process compound requirements like "build a web crawler with authentication, scraping, and proxy rotation" in a single instruction, the AI cannot maintain quality. Claude Task Master analyzes the PRD and automatically breaks it down into small, granular tasks that account for dependencies. A Medium case study article introduces an example where a single PRD automatically generated more than 15 main tasks and more than 24 subtasks.

Problem 3: Task Priority and Dependencies

It is difficult for an AI to autonomously determine the order of "implement A before starting B." Claude Task Master automatically maps dependencies between tasks and has a feature that recommends which task to tackle next.

Key Features

Setup Instructions

What you need: An API key from one or more AI providers (Anthropic, OpenAI, Google Gemini, etc.) and a Node.js environment.

Via MCP Server (Recommended)

This method lets you operate Task Master directly from the AI chat panel in editors like Cursor.

1. Create .cursor/mcp.json in your project root
2. Add the following configuration

{
  "mcpServers": {
    "taskmaster-ai": {
      "command": "npx",
      "args": ["-y", "--package=task-master-ai", "task-master-ai"],
      "env": {
        "ANTHROPIC_API_KEY": "sk-ant-xxxxxxxxxx"
      }
    }
  }
}

3. Restart Cursor
4. Type Initialize taskmaster-ai in my project in the AI chat

Via CLI

npm install -g task-master-ai
task-master init

After initialization, a .taskmaster/ directory is created in the project root, preparing the configuration file and document storage location.

Basic Usage: Steps to Generate Tasks from a PRD

The standard workflow based on the official GitHub tutorial is as follows:

1. Write your product requirements in natural language in .taskmaster/docs/prd.txt
2. Run task-master parse-prd .taskmaster/docs/prd.txt
3. Verify that the task list has been generated in tasks/tasks.json
4. Display the task list with task-master list
5. Check which task to tackle next with task-master next
6. Update the target task to in-progress and start implementation
7. After completion, update the status with task-master set-status --id=1 --status=done
8. Return to step 5 and move on to the next task

When using MCP integration, the CLI commands above can be given as natural language instructions within the editor's AI chat. For example, instructions like "What's the next task?" or "Mark task 3 as done" work as well.

Integration Workflow with Cursor

A SameLogic report introduces a case study of improved "vibe coding" by combining Cursor with Claude Task Master.

The key is maintaining a strict, incremental cycle of "complete task → commit → next task."

1. Create a PRD and have Task Master generate tasks
2. In Cursor's AI chat, instruct it to "check the current task"
3. Have the AI chat implement the specified task
4. Once implementation is complete, git commit and update the task to done
5. Maintain the rhythm of one task per commit and move on to the next task

By repeating this cycle, the AI can always keep track of "what is being built," making it less likely to get lost even in projects spanning multiple sessions.

Supported Editors and AI Models

Supported Editors (via MCP)

• Cursor
• Windsurf
• VS Code (GitHub Copilot Chat)
• Amazon Q Developer CLI
• Lovable
• Roo

Supported AI Models

• Claude (Anthropic) — Opus and Sonnet series
• GPT-4o, o3 / o4-mini (OpenAI)
• Gemini 1.5 Pro / 2.0 (Google)
• Perplexity AI (for research features)
• Grok (xAI)

You can also configure fine-grained role assignments in the settings file, such as using Claude for task generation and planning, Perplexity for research, and GPT-4o for implementation code.

License and Cost Considerations

The license for Claude Task Master is MIT + Commons Clause. Using it for developing your own products is fine, but "selling or hosting Task Master itself as a service" is prohibited.

The cost is only the pay-as-you-go charges for API keys. Using Claude requires an Anthropic API key. Note that the key for Claude.ai subscriptions (Claude Pro / Max) cannot be used via the API, so you will need to separately issue an API key from the Anthropic Console.

Summary

Claude Task Master is a tool that structurally solves the AI coding weaknesses of "context loss" and "handling problems that are too large" by passing a PRD to the AI and having it automatically decompose tasks.

By integrating with editors like Cursor and Windsurf via MCP, task management is completed without leaving the editor, and development can proceed in an incremental cycle of "complete task → commit → next task." Since it supports multiple AI providers, you can also use different models depending on the use case.

For developers who want to incorporate a structured, PRD-based development workflow into AI coding, this is arguably one of the most proven open-source tools available today.