An intelligent, modular, and extensible AI-powered Scrum Master bot that manages and coordinates software development processes across multiple integration services.

1. Modular Architecture

   • Design a highly modular system that can easily integrate with different service providers
   • Maintain a stable, non-breaking public API for all integrations
   • Support plug-and-play integration with various development tools and services

2. Service Integration Abstraction

   • Abstract integration services into domain-specific modules
   • Currently supporting GitHub and JIRA integrations
   • Designed to be easily extended for additional services

3. Web-Based Management Interface

   • Provide a user-friendly web interface for bot configuration and management
   • Enable real-time monitoring and control of bot activities
   • Support customization of bot behaviors and rules

4. Rule-Based Automation

   • Implement a robust rules engine for automated decision-making
   • Support scheduled and event-driven execution of tasks
   • Enable easy configuration and modification of bot behaviors

The AI Scrum Master bot is designed to serve multiple stakeholders in the software development process:

1. Development Teams

   • Self-organizing teams who want to automate routine Scrum activities
   • Teams looking to reduce manual overhead in sprint management
   • Teams that need help with task tracking and coordination
   • Teams working across multiple tools and services

2. Scrum Masters

   • Scrum Masters who want to focus on high-value activities
   • Scrum Masters managing multiple teams
   • Scrum Masters looking for data-driven insights
   • Scrum Masters who need help with administrative tasks

3. Project Managers

   • Project managers overseeing multiple teams
   • Project managers needing better visibility into team performance
   • Project managers looking for automated reporting
   • Project managers managing complex dependencies

4. Team Leads

   • Technical leads who need better team coordination
   • Team leads managing distributed teams
   • Team leads looking for automated workload distribution
   • Team leads needing help with blocker resolution

1. For Teams

   • Reduced administrative overhead
   • Automated task tracking and updates
   • Better visibility into team progress
   • Automated dependency management
   • Streamlined communication

2. For Scrum Masters

   • Focus on coaching and team improvement
   • Automated sprint ceremonies
   • Data-driven retrospectives
   • Automated reporting and metrics
   • Better team insights

3. For Project Managers

   • Real-time project visibility
   • Automated progress tracking
   • Risk identification
   • Resource optimization
   • Cross-team coordination

4. For Team Leads

   • Better team coordination
   • Automated workload distribution
   • Blocker resolution assistance
   • Performance tracking
   • Team capacity management

The bot is designed to be a team-first tool that can be managed by:

1. Team Level

   • Teams can configure and manage their own rules
   • Teams can customize their workflows
   • Teams can set their own priorities
   • Teams can manage their own integrations

2. Organization Level

   • Organizations can set global policies
   • Organizations can manage integrations
   • Organizations can set security policies
   • Organizations can manage access controls

3. Hybrid Approach

   • Teams have autonomy over daily operations
   • Management can set guardrails and policies
   • Both levels can collaborate on configuration
   • Clear separation of concerns

This approach ensures that:

• Teams maintain their autonomy
• Management has necessary oversight
• The tool scales with the organization
• Security and compliance are maintained

The AI Scrum Master bot is designed to handle the following core responsibilities:

1. Sprint Management

   • Sprint planning and estimation
   • Sprint execution monitoring
   • Sprint retrospective facilitation
   • Velocity tracking and reporting

2. Task Management

   • Task creation and assignment
   • Priority management
   • Dependencies tracking
   • Status updates and notifications

3. Team Coordination

   • Team capacity planning
   • Workload distribution
   • Blockers identification and resolution
   • Team communication facilitation

4. Metrics and Reporting

   • Sprint metrics collection
   • Performance analytics
   • Progress reporting
   • Trend analysis

1. Integration Layer

   • Abstract service interfaces
   • Service-specific adapters
   • Data transformation layer

2. Rules Engine

   • Rule definition and management
   • Rule execution scheduler
   • Event handling system

3. Web Interface

   • Configuration management
   • Monitoring dashboard
   • Control panel

4. Domain Services

   • Sprint management service
   • Task management service
   • Team coordination service
   • Metrics service

The bot operates on an event-driven architecture that handles multiple integration streams:

1. Event Streams

   • GitHub Events (PRs, Issues, Comments)
   • JIRA Events (Issues, Comments, Status Changes)
   • System Events (Scheduled Tasks, Alerts)
   • User Events (Web Interface Actions)

2. Event Processing Pipeline

   • Event Ingestion Layer

     • Webhook handlers for external services
     • Scheduled task triggers
     • User action handlers

   • Event Normalization Layer

     • Standardized event format
     • Event enrichment
     • Context preservation

   • Event Routing Layer

     • Event type classification
     • Priority-based routing
     • Load balancing

   • Event Processing Layer

     • Rule evaluation
     • Action execution
     • State management

   • Event Response Layer

     • Action execution
     • External service updates
     • User notifications

3. Event Types

   • Task Events

     • Task created
     • Task updated
     • Task completed
     • Task blocked

   • Sprint Events

     • Sprint started
     • Sprint ended
     • Sprint blocked
     • Sprint metrics updated

   • Team Events

     • Team member availability
     • Workload changes
     • Blockers identified

   • Integration Events

     • Service connection status
     • Sync status
     • Error events

4. Event State Management

   • Event persistence
   • State recovery
   • Event replay capability
   • Audit logging

The bot includes a flexible integration system that allows for easy addition of new service integrations while maintaining a stable event interface:

1. Integration Adapter Pattern

   • Standardized adapter interface
   • Service-specific implementations
   • Clean separation of concerns
   • Version management
   • Capability discovery

2. Adapter Lifecycle

   • Initialization
   • Connection management
   • Validation
   • Event subscription
   • Graceful shutdown

3. Integration Registry

   • Central adapter management
   • Dynamic registration
   • Type-based lookup
   • Connection state management
   • Error handling

4. Capability System

   • Feature discovery
   • Operation validation
   • Event type mapping
   • Service-specific operations
   • Extensible interface

5. Example Integrations

   • GitHub Integration

     • Issue management
     • Pull request handling
     • Repository events
     • Team collaboration

   • JIRA Integration

     • Issue tracking
     • Sprint management
     • Project events
     • Workflow automation

6. Adding New Integrations To add a new integration:

   1. Implement the IntegrationAdapter interface
   2. Define integration capabilities
   3. Handle service-specific events
   4. Map to standard event types
   5. Register with the integration registry

7. Integration Best Practices

   • Maintain backward compatibility
   • Handle service-specific errors
   • Implement retry mechanisms
   • Cache when appropriate
   • Log integration events
   • Monitor connection health

The bot includes a flexible and extensible rules engine that processes events and triggers appropriate actions:

1. Rule Structure

   • Conditions: Define when a rule should be triggered
   • Actions: Define what should happen when conditions are met
   • Priority: Determines the order of rule execution
   • Metadata: Additional information about the rule

2. Built-in Conditions

   • Event Type Matching
   • Event Source Matching
   • Payload Pattern Matching
   • Metadata Matching
   • Time-based Conditions
   • Custom Conditions

3. Built-in Actions

   • Task Updates
   • Notifications
   • Task Creation
   • Sprint Updates
   • Custom Actions

4. Rule Management

   • Dynamic rule addition/removal
   • Rule enabling/disabling
   • Rule prioritization
   • Rule persistence
   • Rule validation

5. Rule Processing

   • Priority-based execution
   • Parallel condition evaluation
   • Sequential action execution
   • Error handling and recovery
   • Rule execution logging

Currently supported integrations:

• GitHub
• JIRA

Future integration possibilities:

• Azure DevOps
• Trello
• Linear
• Custom integrations

The project uses a co-located testing approach with __tests__ directories alongside the source code:

src/
  ├── core/
  │   ├── __tests__/
  │   │   ├── EventPipeline.test.ts
  │   │   └── EventRouter.test.ts
  │   ├── EventPipeline.ts
  │   └── EventRouter.ts
  └── types/
      └── events.ts

This structure was chosen over a separate tests/ directory for several reasons:

1. Co-location: Tests are placed next to the code they're testing, making it easier to find and maintain related files
2. Import Clarity: Relative imports are simpler and more intuitive when tests are co-located
3. IDE Support: Most IDEs provide better navigation and refactoring support when tests are next to their implementation
4. Package Boundaries: When the code is split into packages, co-located tests move with their code
5. Test Discovery: Jest automatically finds and runs tests in __tests__ directories, making test discovery more reliable

[To be added: Installation and setup instructions]

[To be added: Configuration guide]

[To be added: Development setup and guidelines]

[To be added: Contribution guidelines]

ISC