home-lab/research/taskmaster-ai.md

Claude Task Master Research & Integration Plan

Project Overview

Claude Task Master (https://github.com/eyaltoledano/claude-task-master) is an AI-powered task management system that leverages Claude's capabilities for intelligent task breakdown, prioritization, and execution tracking.

Key Features Analysis

Core Capabilities

• Intelligent Task Breakdown: Automatically decomposes complex projects into manageable subtasks
• Context-Aware Planning: Uses AI to understand project requirements and dependencies
• Progress Tracking: Monitors task completion and adjusts plans dynamically
• Natural Language Interface: Allows task management through conversational commands
• Integration Ready: Designed to work with existing development workflows

Technical Architecture

• Backend: Node.js/Python-based task orchestration
• AI Integration: Claude API for task analysis and planning
• Storage: JSON/Database for task persistence
• API: RESTful endpoints for external integrations

Workflow Compatibility Assessment

Current Home-lab Methodology Alignment

✅ Strong Fits

1. Infrastructure-as-Code Philosophy

   • Task Master's structured approach aligns with your NixOS configuration management
   • Can track infrastructure changes as tasks with dependencies

2. Service-Oriented Architecture

   • Fits well with your microservices approach (Transmission, monitoring, etc.)
   • Can manage service deployment and configuration tasks

3. Documentation-Driven Development

   • Integrates with your markdown-based documentation workflow
   • Can auto-generate task documentation and progress reports

⚠️ Considerations

1. Resource Overhead

   • Additional service to manage in your infrastructure
   • API rate limits for Claude integration

2. Data Privacy

   • Task data would be processed by Claude API
   • Need to ensure sensitive infrastructure details are handled appropriately

Integration Strategy

Phase 1: Core Installation & Setup

Prerequisites

# Dependencies for Home-lab integration
- Node.js runtime environment
- Claude API access (Anthropic)
- Docker/Podman for containerization
- NixOS service configuration

Installation Plan

1. Clone and Setup

   cd /home/geir/Home-lab/services
   git clone https://github.com/eyaltoledano/claude-task-master.git taskmaster
   cd taskmaster
   

2. NixOS Service Configuration

   • Create taskmaster.nix service definition
   • Configure API keys and environment variables
   • Set up reverse proxy through existing nginx setup

3. Environment Configuration

   CLAUDE_API_KEY=<your-key>
   TASKMASTER_PORT=3001
   DATABASE_URL=sqlite:///mnt/storage/taskmaster.db
   

Phase 2: GitHub Copilot Integration

Integration Points

1. Code Task Generation

   • Use Copilot to generate coding tasks from repository analysis
   • Automatic task creation from GitHub issues and PRs

2. Development Workflow Enhancement

   // Example integration hook
   interface CopilotTaskBridge {
     generateTasksFromCode(filePath: string): Task[];
     updateTaskProgress(taskId: string, codeChanges: CodeDiff[]): void;
     suggestNextSteps(currentTask: Task): Suggestion[];
   }
   

3. VS Code Extension Development

   • Custom extension to bridge Copilot suggestions with Task Master
   • Real-time task updates based on code changes

Phase 3: Context7 MCP Integration

Model Context Protocol Benefits

1. Unified Context Management

   • Task Master tasks as context for Claude conversations
   • Project state awareness across all AI interactions

2. Cross-Service Communication

   {
     "mcp_config": {
       "services": {
         "taskmaster": {
           "endpoint": "http://sleeper-service:3001/api",
           "capabilities": ["task_management", "progress_tracking"]
         },
         "github_copilot": {
           "integration": "vscode_extension",
           "context_sharing": true
         }
       }
     }
   }
   

3. Context Flow Architecture

   GitHub Copilot → Context7 MCP → Task Master → Claude API
        ↑                                           ↓
   VS Code Editor ←─────── Task Updates ←─────── AI Insights
   

Implementation Roadmap

Week 1: Foundation

• Set up Task Master on sleeper-service
• Configure basic NixOS service
• Test Claude API integration
• Create initial task templates for Home-lab projects

Week 2: GitHub Integration

• Develop Copilot bridge extension
• Set up GitHub webhook integration
• Create automated task generation from repository events
• Test code-to-task mapping

Week 3: MCP Integration

• Implement Context7 MCP protocol support
• Create unified context sharing system
• Develop cross-service communication layer
• Test end-to-end workflow

Week 4: Optimization & Documentation

• Performance tuning and monitoring
• Complete integration documentation
• Create user workflow guides
• Set up backup and recovery procedures

NixOS Service Configuration Preview

# /home/geir/Home-lab/machines/sleeper-service/services/taskmaster.nix
{ config, pkgs, ... }:

{
  services.taskmaster = {
    enable = true;
    port = 3001;
    user = "sma";
    group = "users";
    environmentFile = "/etc/taskmaster/env";
    dataDir = "/mnt/storage/taskmaster";
  };

  # Nginx reverse proxy configuration
  services.nginx.virtualHosts."taskmaster.home-lab" = {
    locations."/" = {
      proxyPass = "http://localhost:3001";
      proxyWebsockets = true;
    };
  };

  # Firewall configuration
  networking.firewall.allowedTCPPorts = [ 3001 ];
}

Benefits for Home-lab Workflow

Immediate Improvements

1. Project Visibility: Clear overview of all infrastructure tasks and their status
2. Dependency Management: Automatic tracking of service dependencies and update sequences
3. Documentation Automation: AI-generated task documentation and progress reports
4. Workflow Optimization: Intelligent task prioritization based on system state

Long-term Value

1. Knowledge Retention: Comprehensive history of infrastructure decisions and changes
2. Onboarding: New team members can quickly understand project structure through task history
3. Compliance: Automated tracking for security updates and maintenance tasks
4. Scalability: Framework for managing larger infrastructure deployments

Risk Assessment & Mitigation

Technical Risks

• API Dependencies: Mitigate with local fallback modes
• Data Loss: Regular backups to /mnt/storage/backups
• Performance Impact: Resource monitoring and limits

Security Considerations

• API Key Management: Use NixOS secrets management
• Network Isolation: Restrict external API access through firewall rules
• Data Encryption: Encrypt sensitive task data at rest

Conclusion

Claude Task Master shows strong alignment with your Home-lab methodology and could significantly enhance project management capabilities. The integration with GitHub Copilot and Context7 MCP would create a powerful AI-assisted development environment that maintains context across all project activities.

Recommendation: Proceed with implementation, starting with Phase 1 to establish the foundation and evaluate real-world performance in your environment.