directus-task-management/README.md

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# Directus Task Management Suite
## Project Overview
A comprehensive task management system built on Directus CMS that integrates seamlessly with the existing AI-powered development ecosystem. This project provides intelligent task orchestration, automated workflow management, and deep integration with Claude Code agents, BMad methodology, and the established Task Master system.
**Status**: Phase 1 - Planning Complete ✅
**Next Phase**: Core Implementation
**Target**: Production-ready task management with AI integration
## Quick Start
### For Developers
```bash
# 1. Review planning documents
cd projects/directus-task-management/planning/
ls -la # See all BMad planning outputs
# 2. Initialize Task Master project for this work
task-master init
task-master parse-prd .taskmaster/docs/prd.txt
# 3. Set up development environment
# (Follow implementation guides in 06-implementation-guides.md)
```
### For Project Managers
- **Vision**: [01-bmad-prd.md](planning/01-bmad-prd.md) - Complete product requirements
- **Sprint Planning**: [05-bmad-user-stories.md](planning/05-bmad-user-stories.md) - 4 sprints, 13 user stories
- **Timeline**: 8 weeks total, 4 phases, incremental delivery
### For System Architects
- **Architecture**: [02-bmad-architecture.md](planning/02-bmad-architecture.md) - 10 collections, 22 MCP tools
- **Integration**: Seamless with existing Directus, Task Master, Claude Code, BMad systems
- **Performance**: Designed for 10,000+ tasks, 100+ projects, 99.9% uptime
## Planning Documents Navigation
### 📋 Phase 1: Strategic Planning (Complete)
| Document | Purpose | Key Deliverables |
|----------|---------|-----------------|
| **[01-bmad-prd.md](planning/01-bmad-prd.md)** | Product Requirements | Vision, features, success metrics, 4-phase roadmap |
| **[02-bmad-architecture.md](planning/02-bmad-architecture.md)** | Technical Architecture | 10 collections, 22 MCP tools, integration patterns |
| **[03-bmad-research-analysis.md](planning/03-bmad-research-analysis.md)** | Market & Technical Research | Industry analysis, Directus patterns, AI integration |
| **[04-bmad-validation.md](planning/04-bmad-validation.md)** | Feasibility Validation | Technical validation, risk assessment, GO decision |
| **[05-bmad-user-stories.md](planning/05-bmad-user-stories.md)** | Sprint Planning | 13 user stories, 4 sprints, velocity estimation |
| **[06-implementation-guides.md](planning/06-implementation-guides.md)** | Development Guides | Code examples, setup instructions, best practices |
## Key Architecture Decisions
### ✅ Validated Technical Decisions
- **Platform**: Directus CMS (existing infrastructure at https://enias.zeabur.app)
- **Integration**: MCP server extension (22 new tools + 40 existing)
- **Sync Strategy**: Bidirectional with Task Master (conflict resolution)
- **AI Integration**: Native Claude Code agent context provision
- **Database**: 10 new collections with strategic indexing
- **Performance**: <100ms response, cursor-based pagination, Redis caching
### 🎯 Success Metrics
- **Efficiency**: 70% time savings in task management workflows
- **AI Integration**: 80% of tasks created through AI assistance
- **Adoption**: 100+ tasks created in first month
- **Reliability**: 99.9% uptime, <1% data integrity issues
- **Team Collaboration**: Web UI enabling multi-user workflows
## Implementation Roadmap
### Phase 2: Core Implementation (Next - Weeks 1-4)
**Sprint 1-2: Foundation & AI Integration**
- [ ] Implement 10 Directus collections with relationships
- [ ] Build 22 MCP tools for task operations
- [ ] Create AI-powered task creation system
- [ ] Set up Claude Code agent context integration
**Deliverables:**
- Functional task management via Directus admin interface
- Natural language task creation
- Agent context provision through MCP tools
- Basic reporting dashboard
### Phase 3: Workflow Integration (Weeks 5-8)
**Sprint 3-4: BMad & Task Master Integration**
- [ ] BMad workflow templates and tracking
- [ ] Task Master bidirectional synchronization
- [ ] Team collaboration features
- [ ] Advanced analytics dashboard
**Deliverables:**
- BMad methodology fully supported
- Seamless Task Master integration
- Team collaboration through web interface
- Production-ready performance optimization
### Phase 4: Strategic Web Management (Future)
**Long-term Vision: Unified Strategic + Tactical Interface**
- Strategic Level: BMad project/epic management via web
- Tactical Level: Task Master functionality via web UI
- Unified Interface: Single pane for both strategic and tactical work
## Integration Overview
### Current System Integration
```
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ Claude Code │ │ BMad Core │ │ Task Master │
│ Agents │ │ Methodology │ │ CLI System │
│ (10+ agents) │ │ (Templates & │ │ (Granular │
│ │ │ Workflows) │ │ Tracking) │
└─────────┬───────┘ └─────────┬────────┘ └─────────┬───────┘
│ │ │
│ ┌───────▼────────┐ │
│ │ │ │
└─────────────►│ Directus │◄─────────────┘
│ Task Management│
│ Suite │
│ │
└────────────────┘
```
### Benefits of Integration
1. **Single Source of Truth**: Centralized task data with web UI
2. **AI-First Design**: Built for agent consumption and automation
3. **Workflow Harmony**: Extends capabilities without disruption
4. **Team Collaboration**: Web interface for multi-user access
5. **Data Integrity**: Backup, recovery, and audit trails
## Next Steps
### Immediate Actions (This Week)
1. **Task Master Setup**: Initialize Task Master project for this implementation work
2. **Environment Preparation**: Set up development environment with Directus access
3. **Schema Design**: Begin implementing the 10 core collections
4. **MCP Development**: Start building the first batch of MCP tools
### Development Approach
1. **BMad Methodology**: Follow established BMad patterns for implementation
2. **TDD Practice**: Write tests first, implement to pass, refactor for quality
3. **Incremental Delivery**: Each sprint delivers working functionality
4. **Documentation First**: Maintain comprehensive docs throughout development
### Resource Requirements
- **Development Time**: 8 weeks (2 developers recommended)
- **Infrastructure**: Existing Directus instance (no additional cost)
- **Integration Effort**: 22 new MCP tools + sync service development
- **Testing**: Comprehensive testing with existing Task Master projects
## Success Criteria
### Technical Success ✅
- All 10 collections operational with proper relationships
- 22 MCP tools providing full task management API
- Bidirectional sync with <5% data conflicts
- <100ms response times under normal load
### User Adoption Success 🎯
- 80% of development work managed through new system
- 70% reduction in context switching between tools
- Team collaboration actively used for shared projects
- Positive user feedback (4.5/5 satisfaction target)
### Business Value Success 💰
- 70% efficiency improvement in task management workflows
- Single interface reducing tool management overhead
- Enhanced AI integration improving development velocity
- Foundation for future strategic planning web interface
## Support & Documentation
### Development Resources
- **Implementation Guide**: [06-implementation-guides.md](planning/06-implementation-guides.md)
- **Architecture Details**: [02-bmad-architecture.md](planning/02-bmad-architecture.md)
- **API Reference**: MCP tools documentation (generated during development)
### Integration Support
- **Directus Instance**: https://enias.zeabur.app/admin
- **MCP Server**: `tools/directus-mcp-server/` (existing + new tools)
- **Task Master CLI**: Existing project with sync service
- **Claude Code Agents**: Context integration through MCP tools
### Project Management
- **Planning Method**: BMad methodology with proven templates
- **Task Tracking**: Task Master CLI (during development) Directus (post-implementation)
- **Progress Reports**: Weekly sprint reviews with stakeholder updates
- **Issue Tracking**: GitHub issues for bug reports and feature requests
---
**Project Lead**: AI Assistant Development Team
**Architecture Review**: Backend Architect & Network Security Architect
**Implementation**: Python Expert Engineer & Code Quality Optimizer
**Documentation**: Documentation Architect
This project represents a strategic enhancement to the existing AI development ecosystem, providing the foundation for scalable, collaborative, and intelligent task management while maintaining compatibility with established workflows and tools.