# 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
```bash
# 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**
   ```bash
   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**
   ```env
   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**
   ```typescript
   // 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**
   ```json
   {
     "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

```nix
# /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.