Convert a PRD to Tasks with Task Master (2026)

Claude Task Master’s best feature is parsing a PRD (Product Requirements Document) into structured, dependency-aware tasks. Here is how to format your PRD for optimal parsing and use the generated tasks with Claude Code.

Prerequisites

  • Task Master installed (npm install -g task-master-ai)
  • A PRD or requirements document (markdown format works best)
  • Claude Code installed (for MCP integration)

Step 1: Format Your PRD

Task Master works best with clearly structured PRDs. Format yours like this:

# Product: User Dashboard
## Overview
A real-time dashboard showing user activity metrics, notifications, and account settings.
## Features
### F1: Activity Feed
- Display last 50 user actions in reverse chronological order
- Each action shows: timestamp, action type, description
- Real-time updates via WebSocket
- Pagination for historical data
### F2: Notification Center
- Display unread notifications with badge count
- Mark individual or all notifications as read
- Notification types: system, mention, update
- Push notification opt-in
### F3: Account Settings
- Edit profile (name, email, avatar)
- Change password with current password verification
- Two-factor authentication toggle
- Email notification preferences
## Technical Requirements
- Frontend: React with TypeScript
- State management: Zustand
- API: REST endpoints on existing Express backend
- Database: PostgreSQL (existing schema)
- Authentication: JWT (existing)

Key formatting rules:

  • Use markdown headers for hierarchy
  • Use bullet points for individual requirements
  • Be specific — vague requirements produce vague tasks
  • Include technical constraints so tasks are implementable

Step 2: Initialize Task Master

cd /path/to/your/project
task-master init

Step 3: Parse the PRD

task-master parse docs/prd.md

Task Master sends the PRD to an AI model, which generates structured tasks. Typical output for the PRD above: 25-40 tasks covering:

  • Database schema changes
  • API endpoint creation
  • Frontend component development
  • Integration and wiring
  • Testing for each feature

Step 4: Review and Adjust Tasks

List the generated tasks:

task-master list

Review the output. Common adjustments needed:

Tasks too granular: Merge related tasks. If “Create ActivityFeed component” and “Add ActivityFeed styles” are separate, consider combining them.

Tasks too broad: Break down tasks that would take more than a day. “Implement notification center” should be split into backend, frontend, and integration tasks.

Missing tasks: Add forgotten items manually:

task-master add "Add WebSocket reconnection logic" --depends-on 5

Wrong dependencies: Fix dependency chains:

task-master update 12 --depends-on 3,7

Step 5: Connect to Claude Code

Configure Task Master as an MCP server:

{
  "mcpServers": {
    "task-master": {
      "command": "task-master",
      "args": ["mcp-server"],
      "cwd": "/path/to/your/project"
    }
  }
}

Start Claude Code and begin executing:

claude

Read the task list and start working on the highest priority unblocked task

Claude reads the task graph, identifies unblocked tasks, and begins implementation. As it completes tasks, it updates their status and moves to the next one.

Verification

# Verify task count
task-master list --status all
# Check for dependency issues
task-master validate
# See execution order
task-master next --count 5

PRD Quality Checklist

The quality of generated tasks depends directly on the quality of your PRD. Before parsing, verify your PRD includes:

  • Clear feature descriptions (not just names)
  • Acceptance criteria for each feature
  • Technical constraints (language, framework, database)
  • User roles and permissions
  • API specifications (endpoints, payloads, responses)
  • UI/UX requirements (if applicable)
  • Performance requirements (response times, throughput)
  • Non-functional requirements (security, accessibility)

A PRD that covers these areas produces 30-50 high-quality tasks. A PRD that only lists feature names produces vague, unhelpful tasks.

Iterating on Generated Tasks

Task generation is not a one-shot process. Plan for 2-3 iterations:

First pass: Generate raw tasks from the PRD. Review for completeness and accuracy.

Second pass: Split broad tasks (anything estimated at more than one day), merge overly granular tasks (anything estimated at less than 30 minutes), and fix dependency ordering.

Third pass: Add tasks the AI missed — deployment steps, documentation, testing infrastructure, and CI/CD configuration often get overlooked.

After the third pass, your task list should be clean enough for Claude to execute autonomously through the MCP integration.

Ongoing PRD Updates

Projects evolve. When requirements change:

  1. Update your PRD document
  2. Re-run task-master parse on the updated PRD
  3. Compare new tasks with existing ones
  4. Manually reconcile: mark already-completed tasks, add new tasks, update changed ones

Task Master does not automatically diff new and existing tasks. The reconciliation is manual but straightforward.

Troubleshooting

Poor task generation: Your PRD may be too vague. Add specific implementation details, technical constraints, and acceptance criteria. Compare your PRD against the quality checklist above.

Circular dependencies: Run task-master validate to detect cycles. Remove or reorder dependencies to break the cycle. Common cause: two tasks that each claim to depend on the other.

Claude picks the wrong task: The MCP integration respects dependency ordering. If Claude picks an unexpected task, check that dependencies are correctly defined. Also verify that completed tasks are marked as such — Claude may pick an already-done task if it was not updated.

Too many tasks: Set a task limit in config to prevent the AI from over-decomposing simple features. Alternatively, generate tasks for one feature at a time rather than the entire PRD.

Tasks do not match your architecture: The AI generates tasks based on common patterns. If your architecture is unusual, review and adjust tasks to match your actual codebase structure.

Next Steps

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