Claude Task Master

A task management system for AI-driven development with Claude, designed to work seamlessly with Cursor AI.

Requirements

• Node.js 14.0.0 or higher
• Anthropic API key (Claude API)
• Anthropic SDK version 0.39.0 or higher
• OpenAI SDK (for Perplexity API integration, optional)

Configuration

The script can be configured through environment variables in a .env file at the root of the project:

Required Configuration

• ANTHROPIC_API_KEY: Your Anthropic API key for Claude

Optional Configuration

• MODEL: Specify which Claude model to use (default: "claude-3-7-sonnet-20250219")
• MAX_TOKENS: Maximum tokens for model responses (default: 4000)
• TEMPERATURE: Temperature for model responses (default: 0.7)
• PERPLEXITY_API_KEY: Your Perplexity API key for research-backed subtask generation
• PERPLEXITY_MODEL: Specify which Perplexity model to use (default: "sonar-medium-online")
• DEBUG: Enable debug logging (default: false)
• LOG_LEVEL: Log level - debug, info, warn, error (default: info)
• DEFAULT_SUBTASKS: Default number of subtasks when expanding (default: 3)
• DEFAULT_PRIORITY: Default priority for generated tasks (default: medium)
• PROJECT_NAME: Override default project name in tasks.json
• PROJECT_VERSION: Override default version in tasks.json

Installation

npm install claude-task-master

Usage

Initialize a new project

npx claude-task-init

This will prompt you for project details and set up a new project with the necessary files and structure.

Important Notes

1. This package uses ES modules. Your package.json should include "type": "module".
2. The Anthropic SDK version should be 0.39.0 or higher.

Troubleshooting

If npx claude-task-init doesn't respond:

Try running it with Node directly:

node node_modules/claude-task-master/scripts/init.js

Or clone the repository and run:

git clone https://github.com/eyaltoledano/claude-task-master.git
cd claude-task-master
node scripts/init.js

Integrating with Cursor AI

Claude Task Master is designed to work seamlessly with Cursor AI, providing a structured workflow for AI-driven development.

Setup with Cursor

1. After initializing your project, open it in Cursor
2. The .cursor/rules/dev_workflow.mdc file is automatically loaded by Cursor, providing the AI with knowledge about the task management system
3. Place your PRD document in the scripts/ directory (e.g., scripts/prd.txt)
4. Open Cursor's AI chat and switch to Agent mode

Initial Task Generation

In Cursor's AI chat, instruct the agent to generate tasks from your PRD:

Please use the dev.js script to parse my PRD and generate tasks. The PRD is located at scripts/prd.txt.

The agent will execute:

node scripts/dev.js parse-prd --input=scripts/prd.txt

This will:

• Parse your PRD document
• Generate a structured tasks.json file with tasks, dependencies, priorities, and test strategies
• The agent will understand this process due to the Cursor rules

Generate Individual Task Files

Next, ask the agent to generate individual task files:

Please generate individual task files from tasks.json

The agent will execute:

node scripts/dev.js generate

This creates individual task files in the tasks/ directory (e.g., task_001.txt, task_002.txt), making it easier to reference specific tasks.

AI-Driven Development Workflow

The Cursor agent is pre-configured (via the rules file) to follow this workflow:

1. Task Discovery and Selection

Ask the agent to list available tasks:

What tasks are available to work on next?

The agent will:

• Run node scripts/dev.js list to see all tasks
• Run node scripts/dev.js list --with-subtasks to see tasks with their subtasks
• Analyze dependencies to determine which tasks are ready to be worked on
• Prioritize tasks based on priority level and ID order
• Suggest the next task(s) to implement

2. Task Implementation

When implementing a task, the agent will:

• Reference the task's details section for implementation specifics
• Consider dependencies on previous tasks
• Follow the project's coding standards
• Create appropriate tests based on the task's testStrategy

You can ask:

Let's implement task 3. What does it involve?

3. Task Verification

Before marking a task as complete, verify it according to:

• The task's specified testStrategy
• Any automated tests in the codebase
• Manual verification if required

4. Task Completion

When a task is completed, tell the agent:

Task 3 is now complete. Please update its status.

The agent will execute:

node scripts/dev.js set-status --id=3 --status=done

5. Handling Implementation Drift

If during implementation, you discover that:

• The current approach differs significantly from what was planned
• Future tasks need to be modified due to current implementation choices
• New dependencies or requirements have emerged

Tell the agent:

We've changed our approach. We're now using Express instead of Fastify. Please update all future tasks to reflect this change.

The agent will execute:

node scripts/dev.js update --from=4 --prompt="Now we are using Express instead of Fastify."

This will rewrite or re-scope subsequent tasks in tasks.json while preserving completed work.

6. Breaking Down Complex Tasks

For complex tasks that need more granularity:

Task 5 seems complex. Can you break it down into subtasks?

The agent will execute:

node scripts/dev.js expand --id=5 --subtasks=3

You can provide additional context:

Please break down task 5 with a focus on security considerations.

The agent will execute:

node scripts/dev.js expand --id=5 --prompt="Focus on security aspects"

You can also expand all pending tasks:

Please break down all pending tasks into subtasks.

The agent will execute:

node scripts/dev.js expand --all

For research-backed subtask generation using Perplexity AI:

Please break down task 5 using research-backed generation.

The agent will execute:

node scripts/dev.js expand --id=5 --research

You can also apply research-backed generation to all tasks:

Please break down all pending tasks using research-backed generation.

The agent will execute:

node scripts/dev.js expand --all --research

Manual Command Reference

While the Cursor agent will handle most commands for you, you can also run them manually:

Parse PRD

npm run parse-prd -- --input=<prd-file.txt>

List Tasks

npm run list

List tasks with a specific status

npm run dev -- list --status=<status>

List tasks with subtasks

npm run dev -- list --with-subtasks

List tasks with a specific status and include subtasks

npm run dev -- list --status=<status> --with-subtasks

Update Tasks

npm run dev -- update --from=<id> --prompt="<prompt>"

Generate Task Files

npm run generate

Set Task Status

npm run dev -- set-status --id=<id> --status=<status>

When marking a task as "done", all of its subtasks will automatically be marked as "done" as well.

Expand Tasks

npm run dev -- expand --id=<id> --subtasks=<number> --prompt="<context>"

or

npm run dev -- expand --all

For research-backed subtask generation:

npm run dev -- expand --id=<id> --research

or

npm run dev -- expand --all --research

Clear Subtasks

# Clear subtasks from a specific task
npm run dev -- clear-subtasks --id=<id>

# Clear subtasks from multiple tasks
npm run dev -- clear-subtasks --id=1,2,3

# Clear subtasks from all tasks
npm run dev -- clear-subtasks --all

Managing Task Dependencies

# Add a dependency to a task
npm run dev -- add-dependency --id=<id> --depends-on=<id>

# Remove a dependency from a task
npm run dev -- remove-dependency --id=<id> --depends-on=<id>

# Validate dependencies without fixing them
npm run dev -- validate-dependencies

# Find and fix invalid dependencies automatically
npm run dev -- fix-dependencies

Task Structure

Tasks in tasks.json have the following structure:

• id: Unique identifier for the task
• title: Brief, descriptive title of the task
• description: Concise description of what the task involves
• status: Current state of the task (pending, done, deferred)
• dependencies: IDs of tasks that must be completed before this task
• priority: Importance level of the task (high, medium, low)
• details: In-depth instructions for implementing the task
• testStrategy: Approach for verifying the task has been completed correctly
• subtasks: List of smaller, more specific tasks that make up the main task

Dependencies are displayed with status indicators (✅ for completed, ⏱️ for pending) throughout the system, making it easy to see which prerequisite tasks are done and which still need work.

Best Practices for AI-Driven Development

1. Start with a detailed PRD: The more detailed your PRD, the better the generated tasks will be.

2. Review generated tasks: After parsing the PRD, review the tasks to ensure they make sense and have appropriate dependencies.

3. Follow the dependency chain: Always respect task dependencies - the Cursor agent will help with this.

4. Update as you go: If your implementation diverges from the plan, use the update command to keep future tasks aligned with your current approach.

5. Break down complex tasks: Use the expand command to break down complex tasks into manageable subtasks.

6. Regenerate task files: After any updates to tasks.json, regenerate the task files to keep them in sync.

7. Communicate context to the agent: When asking the Cursor agent to help with a task, provide context about what you're trying to achieve.

Example Cursor AI Interactions

Starting a new project

I've just initialized a new project with Claude Task Master. I have a PRD at scripts/prd.txt. 
Can you help me parse it and set up the initial tasks?

Working on tasks

What's the next task I should work on? Please consider dependencies and priorities.

Implementing a specific task

I'd like to implement task 4. Can you help me understand what needs to be done and how to approach it?

Managing subtasks

I need to regenerate the subtasks for task 3 with a different approach. Can you help me clear and regenerate them?

Handling changes

We've decided to use MongoDB instead of PostgreSQL. Can you update all future tasks to reflect this change?

Completing work

I've finished implementing the authentication system described in task 2. All tests are passing. 
Please mark it as complete and tell me what I should work on next.

Documentation

For more detailed documentation on the scripts and command-line options, see the scripts/README.md file in your initialized project.

License

MIT

Analyzing Task Complexity

To analyze the complexity of tasks and automatically generate expansion recommendations:

npm run dev -- analyze-complexity

This command:

• Analyzes each task using AI to assess its complexity
• Recommends optimal number of subtasks based on configured DEFAULT_SUBTASKS
• Generates tailored prompts for expanding each task
• Creates a comprehensive JSON report with ready-to-use commands
• Saves the report to scripts/task-complexity-report.json by default

Options:

# Save report to a custom location
npm run dev -- analyze-complexity --output=my-report.json

# Use a specific LLM model
npm run dev -- analyze-complexity --model=claude-3-opus-20240229

# Set a custom complexity threshold (1-10)
npm run dev -- analyze-complexity --threshold=6

# Use an alternative tasks file
npm run dev -- analyze-complexity --file=custom-tasks.json

# Use Perplexity AI for research-backed complexity analysis
npm run dev -- analyze-complexity --research

The generated report contains:

• Complexity analysis for each task (scored 1-10)
• Recommended number of subtasks based on complexity
• AI-generated expansion prompts customized for each task
• Ready-to-run expansion commands directly within each task analysis

Smart Task Expansion

The expand command now automatically checks for and uses the complexity report:

# Expand a task, using complexity report recommendations if available
npm run dev -- expand --id=8

# Expand all tasks, prioritizing by complexity score if a report exists
npm run dev -- expand --all

When a complexity report exists:

• Tasks are automatically expanded using the recommended subtask count and prompts
• When expanding all tasks, they're processed in order of complexity (highest first)
• Research-backed generation is preserved from the complexity analysis
• You can still override recommendations with explicit command-line options

Example workflow:

# Generate the complexity analysis report with research capabilities
npm run dev -- analyze-complexity --research

# Review the report in scripts/task-complexity-report.json

# Expand tasks using the optimized recommendations
npm run dev -- expand --id=8
# or expand all tasks
npm run dev -- expand --all

This integration ensures that task expansion is informed by thorough complexity analysis, resulting in better subtask organization and more efficient development.

Finding the Next Task

To find the next task to work on based on dependencies and status:

npm run dev -- next

This command:

• Identifies tasks that are pending/in-progress and have all dependencies satisfied
• Prioritizes tasks by priority level, dependency count, and task ID
• Displays comprehensive information about the selected task:
  • Basic task details (ID, title, priority, dependencies)
  • Implementation details
  • Subtasks (if they exist)
• Provides contextual suggested actions:
  • Command to mark the task as in-progress
  • Command to mark the task as done
  • Commands for working with subtasks

Example Cursor AI interaction:

What's the next task I should work on?

Viewing Specific Task Details

To view detailed information about a specific task:

npm run dev -- show 1

or

npm run dev -- show --id=1.2

This command:

• Displays comprehensive details about a specific task or subtask
• Shows task status, priority, dependencies, and detailed implementation notes
• For parent tasks, displays all subtasks and their status
• For subtasks, shows parent task relationship
• Provides contextual action suggestions based on the task's state
• Works with both regular tasks and subtasks (using the format taskId.subtaskId)

Example Cursor AI interaction:

Show me the details for task 3

or

Tell me more about subtask 2.1

Task Structure

Tasks in tasks.json have the following structure:

• id: Unique identifier for the task
• title: Brief, descriptive title of the task
• description: Concise description of what the task involves
• status: Current state of the task (pending, done, deferred)
• dependencies: IDs of tasks that must be completed before this task
• priority: Importance level of the task (high, medium, low)
• details: In-depth instructions for implementing the task
• testStrategy: Approach for verifying the task has been completed correctly
• subtasks: List of smaller, more specific tasks that make up the main task

Dependencies are displayed with status indicators (✅ for completed, ⏱️ for pending) throughout the system, making it easy to see which prerequisite tasks are done and which still need work.

Best Practices for AI-Driven Development

1. Start with a detailed PRD: The more detailed your PRD, the better the generated tasks will be.

2. Review generated tasks: After parsing the PRD, review the tasks to ensure they make sense and have appropriate dependencies.

3. Follow the dependency chain: Always respect task dependencies - the Cursor agent will help with this.

4. Update as you go: If your implementation diverges from the plan, use the update command to keep future tasks aligned with your current approach.

5. Break down complex tasks: Use the expand command to break down complex tasks into manageable subtasks.

6. Regenerate task files: After any updates to tasks.json, regenerate the task files to keep them in sync.

7. Communicate context to the agent: When asking the Cursor agent to help with a task, provide context about what you're trying to achieve.

Example Cursor AI Interactions

Starting a new project

I've just initialized a new project with Claude Task Master. I have a PRD at scripts/prd.txt. 
Can you help me parse it and set up the initial tasks?

Working on tasks

What's the next task I should work on? Please consider dependencies and priorities.

Implementing a specific task

I'd like to implement task 4. Can you help me understand what needs to be done and how to approach it?

Managing subtasks

I need to regenerate the subtasks for task 3 with a different approach. Can you help me clear and regenerate them?

Handling changes

We've decided to use MongoDB instead of PostgreSQL. Can you update all future tasks to reflect this change?

Completing work

I've finished implementing the authentication system described in task 2. All tests are passing. 
Please mark it as complete and tell me what I should work on next.

Documentation

For more detailed documentation on the scripts and command-line options, see the scripts/README.md file in your initialized project.

License

MIT

Analyzing Task Complexity

To analyze the complexity of tasks and automatically generate expansion recommendations:

npm run dev -- analyze-complexity

This command:

• Analyzes each task using AI to assess its complexity
• Recommends optimal number of subtasks based on configured DEFAULT_SUBTASKS
• Generates tailored prompts for expanding each task
• Creates a comprehensive JSON report with ready-to-use commands
• Saves the report to scripts/task-complexity-report.json by default

Options:

# Save report to a custom location
npm run dev -- analyze-complexity --output=my-report.json

# Use a specific LLM model
npm run dev -- analyze-complexity --model=claude-3-opus-20240229

# Set a custom complexity threshold (1-10)
npm run dev -- analyze-complexity --threshold=6

# Use an alternative tasks file
npm run dev -- analyze-complexity --file=custom-tasks.json

# Use Perplexity AI for research-backed complexity analysis
npm run dev -- analyze-complexity --research

The generated report contains:

• Complexity analysis for each task (scored 1-10)
• Recommended number of subtasks based on complexity
• AI-generated expansion prompts customized for each task
• Ready-to-run expansion commands directly within each task analysis

Smart Task Expansion

The expand command now automatically checks for and uses the complexity report:

# Expand a task, using complexity report recommendations if available