BMAD-METHOD/bmad-system/semantic-analysis/semantic-understanding-engine.md

# Semantic Understanding Engine

## Deep Semantic Analysis and Intent Understanding for Enhanced BMAD System

The Semantic Understanding Engine provides sophisticated semantic analysis capabilities that understand the meaning, intent, and context behind code, documentation, and development activities, enabling more intelligent and context-aware assistance.

### Semantic Analysis Architecture

#### Multi-Modal Semantic Understanding Framework
```yaml
semantic_analysis_architecture:
  understanding_domains:
    code_semantics:
      - structural_semantics: "Understanding code structure and relationships"
      - functional_semantics: "Understanding what code does and how"
      - intentional_semantics: "Understanding developer intent behind code"
      - behavioral_semantics: "Understanding code behavior and side effects"
      - evolutionary_semantics: "Understanding how code meaning changes over time"
      
    natural_language_semantics:
      - requirement_semantics: "Understanding requirement specifications"
      - documentation_semantics: "Understanding technical documentation"
      - conversation_semantics: "Understanding development discussions"
      - comment_semantics: "Understanding code comments and annotations"
      - query_semantics: "Understanding developer queries and requests"
      
    cross_modal_semantics:
      - code_to_language: "Understanding relationships between code and descriptions"
      - language_to_code: "Understanding how descriptions map to code"
      - multimodal_consistency: "Ensuring consistency across modalities"
      - semantic_bridging: "Bridging semantic gaps between modalities"
      - contextual_disambiguation: "Resolving ambiguity using context"
      
    domain_semantics:
      - business_domain: "Understanding business logic and rules"
      - technical_domain: "Understanding technical concepts and patterns"
      - architectural_domain: "Understanding system architecture semantics"
      - process_domain: "Understanding development process semantics"
      - team_domain: "Understanding team collaboration semantics"
      
  analysis_techniques:
    symbolic_analysis:
      - abstract_syntax_trees: "Structural code analysis"
      - control_flow_graphs: "Code execution flow analysis"
      - data_flow_analysis: "Data movement and transformation analysis"
      - dependency_graphs: "Code dependency relationship analysis"
      - semantic_networks: "Concept relationship networks"
      
    statistical_analysis:
      - distributional_semantics: "Meaning from usage patterns"
      - co_occurrence_analysis: "Semantic relationships from co-occurrence"
      - frequency_analysis: "Semantic importance from frequency"
      - clustering_analysis: "Semantic grouping and categorization"
      - dimensionality_reduction: "Semantic space compression"
      
    neural_analysis:
      - transformer_models: "Deep contextual understanding"
      - attention_mechanisms: "Focus on semantically important parts"
      - embeddings: "Dense semantic representations"
      - sequence_modeling: "Temporal semantic understanding"
      - multimodal_fusion: "Cross-modal semantic integration"
      
    knowledge_based_analysis:
      - ontology_reasoning: "Formal semantic reasoning"
      - rule_based_inference: "Logical semantic deduction"
      - knowledge_graph_traversal: "Semantic relationship exploration"
      - concept_hierarchies: "Hierarchical semantic understanding"
      - semantic_matching: "Semantic similarity and equivalence"
      
  understanding_capabilities:
    intent_recognition:
      - development_intent: "What developer wants to accomplish"
      - code_purpose_intent: "Why code was written this way"
      - modification_intent: "What changes are trying to achieve"
      - architectural_intent: "Intended system design and structure"
      - optimization_intent: "Intended improvements and optimizations"
      
    context_awareness:
      - project_context: "Understanding within project scope"
      - temporal_context: "Understanding time-dependent semantics"
      - team_context: "Understanding within team dynamics"
      - domain_context: "Understanding within business domain"
      - technical_context: "Understanding within technical constraints"
      
    ambiguity_resolution:
      - lexical_disambiguation: "Resolving word meaning ambiguity"
      - syntactic_disambiguation: "Resolving structural ambiguity"
      - semantic_disambiguation: "Resolving meaning ambiguity"
      - pragmatic_disambiguation: "Resolving usage context ambiguity"
      - reference_resolution: "Resolving what entities refer to"
```

#### Semantic Understanding Engine Implementation
```python
import ast
import re
import spacy
import networkx as nx
import numpy as np
from transformers import AutoTokenizer, AutoModel, pipeline
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.decomposition import LatentDirichletAllocation
import torch
import torch.nn.functional as F
from typing import Dict, List, Any, Optional, Tuple, Union
from dataclasses import dataclass
from collections import defaultdict
import asyncio
from datetime import datetime

@dataclass
class SemanticContext:
    """
    Represents semantic context for understanding
    """
    project_context: Dict[str, Any]
    temporal_context: Dict[str, Any]
    team_context: Dict[str, Any]
    domain_context: Dict[str, Any]
    technical_context: Dict[str, Any]

@dataclass
class SemanticUnderstanding:
    """
    Represents the result of semantic analysis
    """
    primary_intent: str
    confidence_score: float
    semantic_concepts: List[str]
    relationships: List[Tuple[str, str, str]]  # (entity1, relation, entity2)
    ambiguities: List[Dict[str, Any]]
    context_factors: List[str]
    recommendations: List[str]

class SemanticUnderstandingEngine:
    """
    Advanced semantic understanding and analysis engine
    """
    
    def __init__(self, config=None):
        self.config = config or {
            'semantic_similarity_threshold': 0.7,
            'intent_confidence_threshold': 0.8,
            'max_ambiguity_candidates': 5,
            'context_window_size': 512
        }
        
        # Initialize NLP components
        self.nlp = spacy.load("en_core_web_sm")
        self.code_bert = AutoModel.from_pretrained("microsoft/codebert-base")
        self.code_tokenizer = AutoTokenizer.from_pretrained("microsoft/codebert-base")
        
        # Initialize specialized analyzers
        self.code_semantic_analyzer = CodeSemanticAnalyzer(self.config)
        self.language_semantic_analyzer = LanguageSemanticAnalyzer(self.config)
        self.intent_recognizer = IntentRecognizer(self.config)
        self.context_analyzer = ContextAnalyzer(self.config)
        self.ambiguity_resolver = AmbiguityResolver(self.config)
        
        # Semantic knowledge base
        self.concept_ontology = ConceptOntology()
        self.semantic_patterns = SemanticPatternLibrary()
        
        # Cross-modal understanding
        self.multimodal_fusion = MultimodalSemanticFusion(self.config)
        
    async def analyze_semantic_understanding(self, input_data, context=None):
        """
        Perform comprehensive semantic analysis of input data
        """
        analysis_session = {
            'session_id': generate_uuid(),
            'input_data': input_data,
            'context': context,
            'understanding_results': {},
            'semantic_insights': {},
            'recommendations': []
        }
        
        # Determine input type and prepare for analysis
        input_analysis = await self.analyze_input_type(input_data)
        analysis_session['input_analysis'] = input_analysis
        
        # Create semantic context
        semantic_context = await self.create_semantic_context(context, input_data)
        analysis_session['semantic_context'] = semantic_context
        
        # Perform domain-specific semantic analysis
        understanding_tasks = []
        
        if input_analysis['has_code']:
            understanding_tasks.append(
                self.analyze_code_semantics(input_data, semantic_context)
            )
        
        if input_analysis['has_natural_language']:
            understanding_tasks.append(
                self.analyze_language_semantics(input_data, semantic_context)
            )
        
        if input_analysis['is_multimodal']:
            understanding_tasks.append(
                self.analyze_multimodal_semantics(input_data, semantic_context)
            )
        
        # Execute analyses in parallel
        understanding_results = await asyncio.gather(*understanding_tasks)
        
        # Integrate results
        integrated_understanding = await self.integrate_semantic_analyses(
            understanding_results,
            semantic_context
        )
        analysis_session['understanding_results'] = integrated_understanding
        
        # Recognize primary intent
        primary_intent = await self.intent_recognizer.recognize_intent(
            integrated_understanding,
            semantic_context
        )
        analysis_session['primary_intent'] = primary_intent
        
        # Resolve ambiguities
        disambiguation_results = await self.ambiguity_resolver.resolve_ambiguities(
            integrated_understanding,
            semantic_context
        )
        analysis_session['disambiguation_results'] = disambiguation_results
        
        # Generate semantic insights
        semantic_insights = await self.generate_semantic_insights(
            integrated_understanding,
            primary_intent,
            disambiguation_results,
            semantic_context
        )
        analysis_session['semantic_insights'] = semantic_insights
        
        # Generate recommendations
        recommendations = await self.generate_semantic_recommendations(
            semantic_insights,
            semantic_context
        )
        analysis_session['recommendations'] = recommendations
        
        return analysis_session
    
    async def analyze_code_semantics(self, input_data, semantic_context):
        """
        Analyze semantic meaning of code
        """
        code_semantics = {
            'structural_semantics': {},
            'functional_semantics': {},
            'intentional_semantics': {},
            'behavioral_semantics': {}
        }
        
        # Extract code from input data
        code_content = self.extract_code_content(input_data)
        
        if not code_content:
            return code_semantics
        
        # Analyze structural semantics
        structural_analysis = await self.code_semantic_analyzer.analyze_structural_semantics(
            code_content,
            semantic_context
        )
        code_semantics['structural_semantics'] = structural_analysis
        
        # Analyze functional semantics
        functional_analysis = await self.code_semantic_analyzer.analyze_functional_semantics(
            code_content,
            semantic_context
        )
        code_semantics['functional_semantics'] = functional_analysis
        
        # Analyze intentional semantics
        intentional_analysis = await self.code_semantic_analyzer.analyze_intentional_semantics(
            code_content,
            semantic_context
        )
        code_semantics['intentional_semantics'] = intentional_analysis
        
        # Analyze behavioral semantics
        behavioral_analysis = await self.code_semantic_analyzer.analyze_behavioral_semantics(
            code_content,
            semantic_context
        )
        code_semantics['behavioral_semantics'] = behavioral_analysis
        
        return code_semantics
    
    async def analyze_language_semantics(self, input_data, semantic_context):
        """
        Analyze semantic meaning of natural language
        """
        language_semantics = {
            'entity_semantics': {},
            'relationship_semantics': {},
            'intent_semantics': {},
            'context_semantics': {}
        }
        
        # Extract natural language from input data
        text_content = self.extract_text_content(input_data)
        
        if not text_content:
            return language_semantics
        
        # Analyze entity semantics
        entity_analysis = await self.language_semantic_analyzer.analyze_entity_semantics(
            text_content,
            semantic_context
        )
        language_semantics['entity_semantics'] = entity_analysis
        
        # Analyze relationship semantics
        relationship_analysis = await self.language_semantic_analyzer.analyze_relationship_semantics(
            text_content,
            semantic_context
        )
        language_semantics['relationship_semantics'] = relationship_analysis
        
        # Analyze intent semantics
        intent_analysis = await self.language_semantic_analyzer.analyze_intent_semantics(
            text_content,
            semantic_context
        )
        language_semantics['intent_semantics'] = intent_analysis
        
        # Analyze context semantics
        context_analysis = await self.language_semantic_analyzer.analyze_context_semantics(
            text_content,
            semantic_context
        )
        language_semantics['context_semantics'] = context_analysis
        
        return language_semantics
    
    async def create_semantic_context(self, context, input_data):
        """
        Create comprehensive semantic context for analysis
        """
        semantic_context = SemanticContext(
            project_context={},
            temporal_context={},
            team_context={},
            domain_context={},
            technical_context={}
        )
        
        if context:
            # Extract project context
            semantic_context.project_context = await self.context_analyzer.extract_project_context(
                context,
                input_data
            )
            
            # Extract temporal context
            semantic_context.temporal_context = await self.context_analyzer.extract_temporal_context(
                context,
                input_data
            )
            
            # Extract team context
            semantic_context.team_context = await self.context_analyzer.extract_team_context(
                context,
                input_data
            )
            
            # Extract domain context
            semantic_context.domain_context = await self.context_analyzer.extract_domain_context(
                context,
                input_data
            )
            
            # Extract technical context
            semantic_context.technical_context = await self.context_analyzer.extract_technical_context(
                context,
                input_data
            )
        
        return semantic_context

class CodeSemanticAnalyzer:
    """
    Specialized analyzer for code semantics
    """
    
    def __init__(self, config):
        self.config = config
        self.ast_analyzer = ASTSemanticAnalyzer()
        self.pattern_matcher = CodePatternMatcher()
        
    async def analyze_structural_semantics(self, code_content, semantic_context):
        """
        Analyze the structural semantic meaning of code
        """
        structural_semantics = {
            'hierarchical_structure': {},
            'modular_relationships': {},
            'dependency_semantics': {},
            'composition_patterns': {}
        }
        
        try:
            # Parse code into AST
            tree = ast.parse(code_content)
            
            # Analyze hierarchical structure
            hierarchical_analysis = await self.ast_analyzer.analyze_hierarchy(tree)
            structural_semantics['hierarchical_structure'] = hierarchical_analysis
            
            # Analyze modular relationships
            modular_analysis = await self.ast_analyzer.analyze_modules(tree)
            structural_semantics['modular_relationships'] = modular_analysis
            
            # Analyze dependency semantics
            dependency_analysis = await self.ast_analyzer.analyze_dependencies(tree)
            structural_semantics['dependency_semantics'] = dependency_analysis
            
            # Identify composition patterns
            composition_analysis = await self.pattern_matcher.identify_composition_patterns(tree)
            structural_semantics['composition_patterns'] = composition_analysis
            
        except SyntaxError as e:
            structural_semantics['error'] = f"Syntax error in code: {str(e)}"
        
        return structural_semantics
    
    async def analyze_functional_semantics(self, code_content, semantic_context):
        """
        Analyze what the code functionally does
        """
        functional_semantics = {
            'primary_functions': [],
            'side_effects': [],
            'data_transformations': [],
            'control_flow_semantics': {}
        }
        
        try:
            tree = ast.parse(code_content)
            
            # Identify primary functions
            primary_functions = await self.identify_primary_functions(tree)
            functional_semantics['primary_functions'] = primary_functions
            
            # Identify side effects
            side_effects = await self.identify_side_effects(tree)
            functional_semantics['side_effects'] = side_effects
            
            # Analyze data transformations
            data_transformations = await self.analyze_data_transformations(tree)
            functional_semantics['data_transformations'] = data_transformations
            
            # Analyze control flow semantics
            control_flow = await self.analyze_control_flow_semantics(tree)
            functional_semantics['control_flow_semantics'] = control_flow
            
        except SyntaxError as e:
            functional_semantics['error'] = f"Syntax error in code: {str(e)}"
        
        return functional_semantics
    
    async def analyze_intentional_semantics(self, code_content, semantic_context):
        """
        Analyze the intent behind the code
        """
        intentional_semantics = {
            'design_intent': {},
            'optimization_intent': {},
            'maintenance_intent': {},
            'feature_intent': {}
        }
        
        # Analyze comments and docstrings for intent clues
        intent_clues = await self.extract_intent_clues(code_content)
        
        # Analyze naming patterns for intent
        naming_intent = await self.analyze_naming_intent(code_content)
        
        # Analyze structural patterns for design intent
        design_intent = await self.analyze_design_intent_patterns(code_content)
        
        # Combine analyses
        intentional_semantics['design_intent'] = design_intent
        intentional_semantics['intent_clues'] = intent_clues
        intentional_semantics['naming_intent'] = naming_intent
        
        return intentional_semantics
    
    async def extract_intent_clues(self, code_content):
        """
        Extract intent clues from comments and docstrings
        """
        intent_clues = {
            'explicit_intents': [],
            'implicit_intents': [],
            'design_rationale': [],
            'todo_items': []
        }
        
        # Extract comments
        comment_pattern = r'#\s*(.+?)(?:\n|$)'
        comments = re.findall(comment_pattern, code_content)
        
        # Extract docstrings
        docstring_pattern = r'"""(.*?)"""'
        docstrings = re.findall(docstring_pattern, code_content, re.DOTALL)
        
        # Analyze comments for intent keywords
        intent_keywords = {
            'explicit': ['todo', 'fix', 'hack', 'temporary', 'optimize'],
            'design': ['because', 'reason', 'purpose', 'goal', 'intent'],
            'improvement': ['improve', 'enhance', 'refactor', 'cleanup']
        }
        
        for comment in comments:
            comment_lower = comment.lower()
            
            # Check for explicit intents
            for keyword in intent_keywords['explicit']:
                if keyword in comment_lower:
                    intent_clues['explicit_intents'].append({
                        'keyword': keyword,
                        'text': comment,
                        'confidence': 0.8
                    })
            
            # Check for design rationale
            for keyword in intent_keywords['design']:
                if keyword in comment_lower:
                    intent_clues['design_rationale'].append({
                        'keyword': keyword,
                        'text': comment,
                        'confidence': 0.7
                    })
        
        return intent_clues

class LanguageSemanticAnalyzer:
    """
    Specialized analyzer for natural language semantics
    """
    
    def __init__(self, config):
        self.config = config
        self.nlp = spacy.load("en_core_web_sm")
        self.entity_linker = EntityLinker()
        self.relation_extractor = RelationExtractor()
        
    async def analyze_entity_semantics(self, text_content, semantic_context):
        """
        Analyze entities and their semantic roles
        """
        entity_semantics = {
            'named_entities': [],
            'concept_entities': [],
            'technical_entities': [],
            'relationship_entities': []
        }
        
        # Process text with spaCy
        doc = self.nlp(text_content)
        
        # Extract named entities
        for ent in doc.ents:
            entity_info = {
                'text': ent.text,
                'label': ent.label_,
                'start': ent.start_char,
                'end': ent.end_char,
                'semantic_type': self.classify_entity_semantics(ent)
            }
            entity_semantics['named_entities'].append(entity_info)
        
        # Extract technical entities
        technical_entities = await self.extract_technical_entities(text_content)
        entity_semantics['technical_entities'] = technical_entities
        
        # Extract concept entities
        concept_entities = await self.extract_concept_entities(text_content, semantic_context)
        entity_semantics['concept_entities'] = concept_entities
        
        return entity_semantics
    
    async def analyze_relationship_semantics(self, text_content, semantic_context):
        """
        Analyze semantic relationships between entities
        """
        relationship_semantics = {
            'explicit_relationships': [],
            'implicit_relationships': [],
            'causal_relationships': [],
            'temporal_relationships': []
        }
        
        # Extract explicit relationships
        explicit_rels = await self.relation_extractor.extract_explicit_relations(text_content)
        relationship_semantics['explicit_relationships'] = explicit_rels
        
        # Infer implicit relationships
        implicit_rels = await self.relation_extractor.infer_implicit_relations(
            text_content,
            semantic_context
        )
        relationship_semantics['implicit_relationships'] = implicit_rels
        
        # Extract causal relationships
        causal_rels = await self.relation_extractor.extract_causal_relations(text_content)
        relationship_semantics['causal_relationships'] = causal_rels
        
        # Extract temporal relationships
        temporal_rels = await self.relation_extractor.extract_temporal_relations(text_content)
        relationship_semantics['temporal_relationships'] = temporal_rels
        
        return relationship_semantics
    
    def classify_entity_semantics(self, entity):
        """
        Classify the semantic type of an entity
        """
        semantic_mappings = {
            'PERSON': 'agent',
            'ORG': 'organization',
            'PRODUCT': 'artifact',
            'EVENT': 'process',
            'DATE': 'temporal',
            'TIME': 'temporal',
            'MONEY': 'resource',
            'PERCENT': 'metric'
        }
        
        return semantic_mappings.get(entity.label_, 'unknown')

class IntentRecognizer:
    """
    Recognizes intent from semantic analysis results
    """
    
    def __init__(self, config):
        self.config = config
        self.intent_patterns = {
            'information_seeking': [
                'what', 'how', 'why', 'when', 'where', 'explain', 'describe'
            ],
            'problem_solving': [
                'fix', 'solve', 'resolve', 'debug', 'troubleshoot'
            ],
            'implementation': [
                'implement', 'create', 'build', 'develop', 'code'
            ],
            'optimization': [
                'optimize', 'improve', 'enhance', 'faster', 'better'
            ],
            'analysis': [
                'analyze', 'review', 'examine', 'evaluate', 'assess'
            ]
        }
    
    async def recognize_intent(self, semantic_understanding, context):
        """
        Recognize primary intent from semantic understanding
        """
        intent_scores = defaultdict(float)
        
        # Analyze language semantics for intent keywords
        if 'language_semantics' in semantic_understanding:
            lang_semantics = semantic_understanding['language_semantics']
            
            for intent_type, keywords in self.intent_patterns.items():
                for keyword in keywords:
                    if any(keyword in str(analysis).lower() 
                          for analysis in lang_semantics.values()):
                        intent_scores[intent_type] += 1.0
        
        # Analyze code semantics for implementation intent
        if 'code_semantics' in semantic_understanding:
            code_semantics = semantic_understanding['code_semantics']
            
            # Check for implementation patterns
            if code_semantics.get('functional_semantics', {}).get('primary_functions'):
                intent_scores['implementation'] += 2.0
            
            # Check for optimization patterns
            if any('optimization' in str(analysis).lower() 
                  for analysis in code_semantics.get('intentional_semantics', {}).values()):
                intent_scores['optimization'] += 1.5
        
        # Determine primary intent
        if intent_scores:
            primary_intent = max(intent_scores.items(), key=lambda x: x[1])
            confidence = min(primary_intent[1] / sum(intent_scores.values()), 1.0)
            
            return {
                'intent': primary_intent[0],
                'confidence': confidence,
                'all_scores': dict(intent_scores)
            }
        
        return {
            'intent': 'unknown',
            'confidence': 0.0,
            'all_scores': {}
        }

class AmbiguityResolver:
    """
    Resolves semantic ambiguities using context and knowledge
    """
    
    def __init__(self, config):
        self.config = config
        
    async def resolve_ambiguities(self, semantic_understanding, context):
        """
        Resolve identified ambiguities in semantic understanding
        """
        disambiguation_results = {
            'resolved_ambiguities': [],
            'remaining_ambiguities': [],
            'confidence_scores': {}
        }
        
        # Identify potential ambiguities
        ambiguities = await self.identify_ambiguities(semantic_understanding)
        
        # Resolve each ambiguity using context
        for ambiguity in ambiguities:
            resolution = await self.resolve_single_ambiguity(ambiguity, context)
            
            if resolution['confidence'] > self.config['intent_confidence_threshold']:
                disambiguation_results['resolved_ambiguities'].append(resolution)
            else:
                disambiguation_results['remaining_ambiguities'].append(ambiguity)
        
        return disambiguation_results
    
    async def identify_ambiguities(self, semantic_understanding):
        """
        Identify potential ambiguities in semantic understanding
        """
        ambiguities = []
        
        # Check for multiple possible intents
        if 'language_semantics' in semantic_understanding:
            intent_semantics = semantic_understanding['language_semantics'].get('intent_semantics', {})
            
            if len(intent_semantics.get('possible_intents', [])) > 1:
                ambiguities.append({
                    'type': 'intent_ambiguity',
                    'candidates': intent_semantics['possible_intents'],
                    'context': 'multiple_intents_detected'
                })
        
        # Check for ambiguous entity references
        if 'language_semantics' in semantic_understanding:
            entity_semantics = semantic_understanding['language_semantics'].get('entity_semantics', {})
            
            for entity in entity_semantics.get('named_entities', []):
                if entity.get('ambiguous', False):
                    ambiguities.append({
                        'type': 'entity_reference_ambiguity',
                        'entity': entity['text'],
                        'candidates': entity.get('candidates', []),
                        'context': 'ambiguous_entity_reference'
                    })
        
        return ambiguities
    
    async def resolve_single_ambiguity(self, ambiguity, context):
        """
        Resolve a single ambiguity using available context
        """
        resolution = {
            'ambiguity_type': ambiguity['type'],
            'original_candidates': ambiguity.get('candidates', []),
            'resolved_value': None,
            'confidence': 0.0,
            'resolution_method': 'context_based'
        }
        
        if ambiguity['type'] == 'intent_ambiguity':
            # Use context to determine most likely intent
            resolution = await self.resolve_intent_ambiguity(ambiguity, context)
        elif ambiguity['type'] == 'entity_reference_ambiguity':
            # Use context to determine most likely entity reference
            resolution = await self.resolve_entity_ambiguity(ambiguity, context)
        
        return resolution
```

### Semantic Analysis Commands

```bash
# Semantic understanding and analysis
bmad semantic analyze --input "code-file.py" --context "project-requirements"
bmad semantic understand --query "implement user authentication" --deep-analysis
bmad semantic extract --concepts --from "documentation/" --relationships

# Intent recognition and disambiguation
bmad semantic intent --recognize --from "user-query" --confidence-threshold 0.8
bmad semantic disambiguate --ambiguous-terms --use-context
bmad semantic clarify --unclear-requirements --suggest-interpretations

# Cross-modal semantic analysis
bmad semantic bridge --code-to-language --explain "function-implementation"
bmad semantic consistency --check --across "code,docs,comments"
bmad semantic map --requirements-to-code --show-gaps

# Semantic insights and recommendations
bmad semantic insights --generate --focus "intent-code-alignment"
bmad semantic recommend --improvements --based-on-semantics
bmad semantic export --understanding --format "knowledge-graph"
```

<function_calls>
<invoke name="TodoWrite">
<parameter name="todos">[{"content": "Phase 1: Core Intelligence Foundation - COMPLETED \u2705", "status": "completed", "priority": "high", "id": "phase1"}, {"content": "Phase 2: LLM Integration and Knowledge Management - COMPLETED \u2705", "status": "completed", "priority": "high", "id": "phase2"}, {"content": "Create LLM Abstraction Framework", "status": "completed", "priority": "high", "id": "2.1"}, {"content": "Implement Knowledge Management Core", "status": "completed", "priority": "high", "id": "2.2"}, {"content": "Build Cross-Project Learning System", "status": "completed", "priority": "high", "id": "2.3"}, {"content": "Develop Advanced Memory Architecture", "status": "completed", "priority": "high", "id": "2.4"}, {"content": "Create Universal Workflow Engine", "status": "completed", "priority": "high", "id": "2.5"}, {"content": "Implement Knowledge Discovery Platform", "status": "completed", "priority": "high", "id": "2.6"}, {"content": "Build Semantic Analysis Engine", "status": "completed", "priority": "high", "id": "2.7"}, {"content": "Phase 3: Advanced Intelligence and Claude Code Integration", "status": "pending", "priority": "medium", "id": "phase3"}, {"content": "Phase 4: Self-Optimization and Enterprise Features", "status": "pending", "priority": "medium", "id": "phase4"}]