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BMAD-METHOD/bmad-system/integration/persona-intelligence-bridge.md

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# Persona Intelligence Bridge
## Integration Layer Between BMAD Intelligence System and Existing Personas
The Persona Intelligence Bridge seamlessly connects the new BMAD intelligence system with existing personas, enhancing their capabilities while maintaining their unique characteristics and responsibilities.
### Integration Architecture
#### Persona Enhancement Framework
```yaml
persona_enhancement:
intelligence_augmentation:
core_enhancements:
- enhanced_decision_making: "Access to pattern intelligence and decision engine"
- memory_integration: "Access to project memory and solution repository"
- error_prevention: "Real-time error prevention based on historical patterns"
- communication_enhancement: "Inter-persona messaging and collaboration"
- rule_application: "Dynamic rule application based on context"
persona_specific_enhancements:
analyst:
- deep_pattern_recognition: "Enhanced ability to identify trends and patterns"
- cross_project_insights: "Access to insights from similar projects"
- automated_requirement_analysis: "AI-assisted requirement extraction"
- stakeholder_behavior_patterns: "Understanding of stakeholder communication patterns"
architect:
- architecture_pattern_library: "Access to proven architectural solutions"
- technology_decision_support: "Data-driven technology selection"
- scalability_prediction: "Predictive analysis for architecture decisions"
- integration_complexity_assessment: "Automated complexity analysis"
dev:
- code_pattern_intelligence: "Smart code pattern recognition and application"
- implementation_guidance: "Step-by-step guidance from solution repository"
- error_prevention_assistance: "Real-time coding error prevention"
- optimization_suggestions: "Performance and maintainability improvements"
qa:
- test_pattern_intelligence: "Intelligent test case generation"
- defect_prediction: "Predictive defect analysis based on patterns"
- quality_metric_tracking: "Automated quality assessment"
- regression_prevention: "Prevention of previously encountered issues"
pm:
- project_success_patterns: "Access to successful project management patterns"
- risk_prediction: "Early warning system for project risks"
- resource_optimization: "Intelligent resource allocation suggestions"
- timeline_estimation: "Data-driven timeline predictions"
```
#### Enhanced Persona Definitions
```python
async def enhance_persona_with_intelligence(persona_definition, intelligence_system):
"""
Enhance existing persona with intelligence system capabilities
"""
enhanced_persona = {
**persona_definition,
'intelligence_enhancements': {
'pattern_access': await connect_to_pattern_intelligence(persona_definition['name']),
'memory_access': await connect_to_memory_system(persona_definition['name']),
'communication_protocol': await setup_persona_messaging(persona_definition['name']),
'rule_engine_access': await connect_to_rule_engine(persona_definition['name']),
'error_prevention': await setup_error_prevention(persona_definition['name'])
},
'enhanced_capabilities': generate_enhanced_capabilities(persona_definition, intelligence_system),
'collaboration_protocols': define_collaboration_protocols(persona_definition),
'learning_systems': setup_persona_learning(persona_definition)
}
return enhanced_persona
async def generate_enhanced_capabilities(persona_definition, intelligence_system):
"""
Generate enhanced capabilities based on persona role and intelligence system
"""
base_capabilities = persona_definition.get('capabilities', [])
persona_name = persona_definition['name']
# Role-specific intelligence enhancements
if persona_name == 'analyst':
intelligence_enhancements = [
'pattern_based_requirement_analysis',
'stakeholder_behavior_prediction',
'market_trend_correlation',
'risk_pattern_identification',
'user_journey_optimization'
]
elif persona_name == 'architect':
intelligence_enhancements = [
'architectural_pattern_matching',
'technology_compatibility_analysis',
'scalability_bottleneck_prediction',
'integration_complexity_assessment',
'technical_debt_prevention'
]
elif persona_name == 'dev':
intelligence_enhancements = [
'code_pattern_suggestion',
'implementation_path_optimization',
'bug_prevention_assistance',
'performance_optimization_guidance',
'maintainability_improvement'
]
elif persona_name == 'qa':
intelligence_enhancements = [
'intelligent_test_case_generation',
'defect_pattern_prediction',
'quality_metric_automation',
'regression_prevention_analysis',
'test_coverage_optimization'
]
elif persona_name == 'pm':
intelligence_enhancements = [
'project_success_prediction',
'resource_optimization_analysis',
'timeline_accuracy_improvement',
'stakeholder_satisfaction_tracking',
'scope_creep_prevention'
]
else:
# Generic enhancements for other personas
intelligence_enhancements = [
'pattern_recognition_assistance',
'decision_support_enhancement',
'error_prevention_guidance',
'collaboration_optimization'
]
return {
'base_capabilities': base_capabilities,
'intelligence_enhancements': intelligence_enhancements,
'combined_capabilities': base_capabilities + intelligence_enhancements
}
```
### Persona Integration Implementation
#### Intelligence-Enhanced Persona Loading
```python
async def load_intelligence_enhanced_persona(persona_name, project_context):
"""
Load persona with full intelligence system integration
"""
# Load base persona definition
base_persona = await load_base_persona_definition(persona_name)
# Connect to intelligence systems
intelligence_connections = {
'pattern_intelligence': await connect_persona_to_pattern_intelligence(
persona_name,
project_context
),
'memory_system': await connect_persona_to_memory_system(
persona_name,
project_context
),
'decision_engine': await connect_persona_to_decision_engine(
persona_name,
project_context
),
'rule_engine': await connect_persona_to_rule_engine(
persona_name,
project_context
),
'error_prevention': await connect_persona_to_error_prevention(
persona_name,
project_context
)
}
# Enhance persona with intelligence
enhanced_persona = await enhance_persona_with_intelligence(
base_persona,
intelligence_connections
)
# Setup persona-specific workflows
enhanced_workflows = await create_enhanced_workflows(
enhanced_persona,
intelligence_connections,
project_context
)
# Initialize persona with Claude Code integration
claude_integration = await setup_persona_claude_integration(
enhanced_persona,
intelligence_connections
)
return {
'persona': enhanced_persona,
'intelligence_connections': intelligence_connections,
'enhanced_workflows': enhanced_workflows,
'claude_integration': claude_integration,
'initialization_status': 'ready'
}
async def create_enhanced_workflows(persona, intelligence_connections, project_context):
"""
Create intelligence-enhanced workflows for persona
"""
base_workflows = persona.get('workflows', [])
persona_name = persona['name']
# Create persona-specific enhanced workflows
if persona_name == 'analyst':
enhanced_workflows = await create_analyst_enhanced_workflows(
intelligence_connections,
project_context
)
elif persona_name == 'architect':
enhanced_workflows = await create_architect_enhanced_workflows(
intelligence_connections,
project_context
)
elif persona_name == 'dev':
enhanced_workflows = await create_dev_enhanced_workflows(
intelligence_connections,
project_context
)
elif persona_name == 'qa':
enhanced_workflows = await create_qa_enhanced_workflows(
intelligence_connections,
project_context
)
elif persona_name == 'pm':
enhanced_workflows = await create_pm_enhanced_workflows(
intelligence_connections,
project_context
)
else:
enhanced_workflows = await create_generic_enhanced_workflows(
intelligence_connections,
project_context
)
return {
'base_workflows': base_workflows,
'enhanced_workflows': enhanced_workflows,
'combined_workflows': base_workflows + enhanced_workflows
}
```
#### Role-Specific Intelligence Integration
```python
async def create_analyst_enhanced_workflows(intelligence_connections, project_context):
"""
Create enhanced workflows for analyst persona
"""
return [
{
'name': 'intelligent_requirement_analysis',
'description': 'AI-enhanced requirement analysis using pattern recognition',
'steps': [
{
'action': 'analyze_stakeholder_input',
'intelligence_support': 'pattern_recognition',
'tools': ['Read', 'Grep', 'pattern_intelligence']
},
{
'action': 'identify_requirement_patterns',
'intelligence_support': 'memory_recall',
'tools': ['memory_system', 'decision_engine']
},
{
'action': 'predict_missing_requirements',
'intelligence_support': 'pattern_extrapolation',
'tools': ['pattern_intelligence', 'solution_repository']
},
{
'action': 'validate_requirement_completeness',
'intelligence_support': 'completeness_analysis',
'tools': ['rule_engine', 'error_prevention']
}
]
},
{
'name': 'stakeholder_behavior_analysis',
'description': 'Understand stakeholder communication patterns',
'steps': [
{
'action': 'analyze_communication_history',
'intelligence_support': 'pattern_recognition',
'tools': ['memory_system', 'pattern_intelligence']
},
{
'action': 'predict_stakeholder_needs',
'intelligence_support': 'behavioral_prediction',
'tools': ['pattern_intelligence', 'decision_engine']
},
{
'action': 'optimize_communication_strategy',
'intelligence_support': 'strategy_optimization',
'tools': ['solution_repository', 'rule_engine']
}
]
}
]
async def create_architect_enhanced_workflows(intelligence_connections, project_context):
"""
Create enhanced workflows for architect persona
"""
return [
{
'name': 'intelligent_architecture_design',
'description': 'AI-assisted architectural decision making',
'steps': [
{
'action': 'analyze_project_requirements',
'intelligence_support': 'requirement_analysis',
'tools': ['Read', 'pattern_intelligence', 'memory_system']
},
{
'action': 'search_architectural_patterns',
'intelligence_support': 'pattern_matching',
'tools': ['solution_repository', 'pattern_intelligence']
},
{
'action': 'evaluate_technology_options',
'intelligence_support': 'decision_support',
'tools': ['decision_engine', 'memory_system']
},
{
'action': 'predict_scalability_challenges',
'intelligence_support': 'predictive_analysis',
'tools': ['pattern_intelligence', 'error_prevention']
},
{
'action': 'optimize_architecture_design',
'intelligence_support': 'optimization_analysis',
'tools': ['rule_engine', 'solution_repository']
}
]
},
{
'name': 'technical_debt_prevention',
'description': 'Proactive technical debt identification and prevention',
'steps': [
{
'action': 'analyze_code_patterns',
'intelligence_support': 'pattern_analysis',
'tools': ['Grep', 'pattern_intelligence', 'rule_engine']
},
{
'action': 'identify_debt_indicators',
'intelligence_support': 'debt_detection',
'tools': ['error_prevention', 'memory_system']
},
{
'action': 'recommend_refactoring_strategies',
'intelligence_support': 'strategy_recommendation',
'tools': ['solution_repository', 'decision_engine']
}
]
}
]
async def create_dev_enhanced_workflows(intelligence_connections, project_context):
"""
Create enhanced workflows for dev persona
"""
return [
{
'name': 'intelligent_code_implementation',
'description': 'AI-guided code implementation with pattern assistance',
'steps': [
{
'action': 'analyze_implementation_requirements',
'intelligence_support': 'requirement_analysis',
'tools': ['Read', 'memory_system', 'pattern_intelligence']
},
{
'action': 'search_code_patterns',
'intelligence_support': 'pattern_matching',
'tools': ['solution_repository', 'pattern_intelligence']
},
{
'action': 'generate_implementation_plan',
'intelligence_support': 'planning_assistance',
'tools': ['decision_engine', 'rule_engine']
},
{
'action': 'implement_with_error_prevention',
'intelligence_support': 'error_prevention',
'tools': ['Write', 'Edit', 'error_prevention', 'rule_engine']
},
{
'action': 'validate_implementation_quality',
'intelligence_support': 'quality_validation',
'tools': ['Bash', 'rule_engine', 'pattern_intelligence']
}
]
},
{
'name': 'performance_optimization_assistance',
'description': 'Intelligent performance optimization guidance',
'steps': [
{
'action': 'analyze_performance_patterns',
'intelligence_support': 'performance_analysis',
'tools': ['Grep', 'pattern_intelligence', 'memory_system']
},
{
'action': 'identify_optimization_opportunities',
'intelligence_support': 'opportunity_identification',
'tools': ['solution_repository', 'pattern_intelligence']
},
{
'action': 'apply_optimization_patterns',
'intelligence_support': 'pattern_application',
'tools': ['Edit', 'MultiEdit', 'rule_engine']
}
]
}
]
```
### Integration Orchestration
#### Master Integration Controller
```python
async def initialize_persona_intelligence_integration(project_context):
"""
Initialize complete integration between personas and intelligence system
"""
integration_session = {
'session_id': generate_uuid(),
'project_context': project_context,
'integration_status': {},
'active_personas': {},
'intelligence_system': {},
'communication_channels': {}
}
# Initialize intelligence system
intelligence_system = await initialize_intelligence_system(project_context)
integration_session['intelligence_system'] = intelligence_system
# Load and enhance each active persona
active_persona_names = determine_active_personas(project_context)
for persona_name in active_persona_names:
try:
# Load intelligence-enhanced persona
enhanced_persona = await load_intelligence_enhanced_persona(
persona_name,
project_context
)
integration_session['active_personas'][persona_name] = enhanced_persona
integration_session['integration_status'][persona_name] = 'success'
except Exception as e:
integration_session['integration_status'][persona_name] = {
'status': 'failed',
'error': str(e)
}
# Setup inter-persona communication with intelligence
communication_channels = await setup_intelligence_enhanced_communication(
integration_session['active_personas'],
intelligence_system
)
integration_session['communication_channels'] = communication_channels
# Validate integration completeness
validation_result = await validate_integration_completeness(integration_session)
integration_session['validation'] = validation_result
return integration_session
async def setup_intelligence_enhanced_communication(personas, intelligence_system):
"""
Setup communication channels between personas with intelligence enhancement
"""
communication_setup = {
'message_routing': await setup_intelligent_message_routing(personas),
'collaboration_patterns': await define_intelligent_collaboration_patterns(personas),
'conflict_resolution': await setup_intelligent_conflict_resolution(personas),
'decision_coordination': await setup_intelligent_decision_coordination(personas),
'knowledge_sharing': await setup_intelligent_knowledge_sharing(personas)
}
return communication_setup
```
### Claude Code Integration Commands
```bash
# Persona intelligence integration commands
bmad personas enhance --all --with-intelligence
bmad personas load --name "architect" --intelligence-enabled
bmad personas status --show-intelligence-connections
# Integration management
bmad integration init --personas "analyst,architect,dev,qa" --intelligence-full
bmad integration validate --check-connections --test-communication
bmad integration optimize --based-on-usage --improve-collaboration
# Enhanced persona workflows
bmad workflow run --persona "analyst" --workflow "intelligent_requirement_analysis"
bmad workflow enhance --persona "dev" --add-intelligence-steps
bmad workflow collaborate --personas "architect,dev" --with-intelligence-mediation
```
This Persona Intelligence Bridge seamlessly integrates the new BMAD intelligence system with existing personas, enhancing their capabilities while preserving their unique roles and responsibilities. The integration provides each persona with access to pattern intelligence, memory systems, error prevention, and enhanced collaboration capabilities.
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