Hello i really appreciate the button to generate a git commit message is there any possibility to set a prompt ? then i can make AI use a conventional format
Thanks
Yes… I massively direct my bots to document as they go. Here is an example status report as it goes along…
Here is a list of the status report rmds I make it make, as an example on one project:
Would you like any specific adive?
===
SwarmHub Project Summary - 2023-12-24
Project Overview
The SwarmHub project is a comprehensive platform for managing AI agents and their interactions within a distributed system. The project encompasses several key components and systems that work together to provide a robust, scalable, and secure environment for AI agent orchestration.
Core Components
1. Agent Orchestration
- Implemented task queue system for managing agent workloads
- Created context management for maintaining agent state
- Built metrics collection for monitoring agent performance
- Established task lifecycle management
2. Communication System
- Developed event bus for system-wide communication
- Implemented message queue for reliable message delivery
- Created communication channels for different types of interactions
- Built metrics collection for monitoring communication patterns
3. Configuration Management
- Set up environment variable management
- Implemented configuration validation
- Created configuration update system
- Built configuration API for dynamic updates
4. Security System
- Implemented authentication and authorization
- Created token management system
- Built encryption service for sensitive data
- Established security monitoring and logging
5. Testing Framework
- Set up comprehensive test suite
- Implemented coverage collection
- Created test runners for different test types
- Built test reporting system
Database Schema
1. Core Tables
-- Agent management
swarm_hub.agent_profiles
swarm_hub.agent_capabilities
swarm_hub.agent_metrics
-- Project management
swarm_hub.projects
swarm_hub.project_requirements
swarm_hub.project_metrics
-- Task management
swarm_hub.task_queue
swarm_hub.task_executions
swarm_hub.task_metrics
-- Event management
swarm_hub.events
swarm_hub.event_subscriptions
swarm_hub.message_queue
-- Security
swarm_hub.users
swarm_hub.roles
swarm_hub.permissions
2. Materialized Views
-- Performance metrics
swarm_hub.mv_agent_performance_metrics
swarm_hub.mv_project_performance_metrics
swarm_hub.mv_task_performance_metrics
Current Status
1. Completed Items
- Basic database schema
- Core agent management
- Project management
- Task queue system
- Event system
- Security framework
- Test framework
2. In Progress
- Advanced metrics collection
- Machine learning integration
- Performance optimization
- Documentation
- Frontend development
3. Planned Features
- Agent collaboration system
- Advanced project matching
- Real-time monitoring
- Predictive analytics
- Auto-scaling system
Technical Insights
1. Performance
- Using materialized views for efficient querying
- Implementing caching strategies
- Optimizing database indexes
- Monitoring system metrics
2. Scalability
- Designed for horizontal scaling
- Using message queues for reliability
- Implementing connection pooling
- Planning for high availability
3. Security
- Implementing JWT-based authentication
- Using role-based access control
- Encrypting sensitive data
- Monitoring security events
Next Steps
1. Immediate Priorities
- Complete metrics implementation
- Finalize frontend development
- Implement remaining API endpoints
- Set up monitoring dashboards
- Complete documentation
2. Short-term Goals
- Enhance agent capabilities
- Improve project matching
- Optimize performance
- Increase test coverage
- Deploy beta version
3. Long-term Vision
- Implement AI-driven improvements
- Expand agent ecosystem
- Enhance collaboration features
- Develop advanced analytics
- Scale infrastructure
Development Workflow
1. Code Management
# Branch strategy
main # Production-ready code
development # Integration branch
feature/* # Feature branches
bugfix/* # Bug fix branches
release/* # Release branches
# Version control
git flow init
git flow feature start feature-name
git flow feature finish feature-name
2. Deployment Pipeline
# CI/CD stages
stages:
- test
- build
- deploy
# Testing
test:
script:
- npm install
- npm run test
- npm run lint
# Build
build:
script:
- docker build -t swarm-hub .
- docker push swarm-hub
# Deploy
deploy:
script:
- kubectl apply -f k8s/
Technical Debt
1. Current Issues
- Query optimization needed
- Cache implementation incomplete
- Documentation gaps
- Test coverage below target
- Performance bottlenecks
2. Planned Solutions
- Implement query optimization
- Complete cache system
- Update documentation
- Increase test coverage
- Profile and optimize
Monitoring Strategy
1. Metrics Collection
- Agent performance metrics
- System health metrics
- API performance metrics
- Database metrics
- Security metrics
2. Alerting System
- Performance thresholds
- Error rate monitoring
- Resource utilization
- Security events
- System health
Notes
1. Best Practices
- Follow coding standards
- Regular code reviews
- Comprehensive testing
- Security first approach
- Performance monitoring
2. Maintenance
- Regular updates
- Security patches
- Performance tuning
- Database maintenance
- System backups
3. Documentation
- Keep API docs updated
- Maintain change logs
- Document decisions
- Update diagrams
- Track metrics
Future Considerations
1. Technology Updates
- Evaluate new technologies
- Plan version upgrades
- Consider alternatives
- Monitor trends
- Research improvements
2. Scale Planning
- Infrastructure scaling
- Database scaling
- Cache scaling
- Network scaling
- Cost optimization
3. Feature Roadmap
- Advanced analytics
- Machine learning
- Real-time features
- Mobile support
- API expansion