ajbcoding/claude-skill-eval/moai-alfred-proactive-suggestions

# AI-powered proactive suggestions architecture optimization with Context7
class ProactiveSuggestionsArchitectOptimizer:
    def __init__(self):
        self.context7_client = Context7Client()
        self.productivity_analyzer = ProductivityAnalyzer()
        self.suggestion_engine = SuggestionEngine()
    
    async def design_optimal_suggestions_architecture(self, 
                                                     requirements: ProductivityRequirements) -> ProactiveSuggestionsArchitecture:
        """Design optimal proactive suggestions architecture using AI analysis."""
        
        # Get latest productivity and AI assistance documentation via Context7
        productivity_docs = await self.context7_client.get_library_docs(
            context7_library_id='/productivity/docs',
            topic="developer productivity workflow optimization 2025",
            tokens=3000
        )
        
        ai_docs = await self.context7_client.get_library_docs(
            context7_library_id='/ai-assistance/docs',
            topic="intelligent suggestions context awareness 2025",
            tokens=2000
        )
        
        # Optimize suggestion engine
        suggestion_configuration = self.suggestion_engine.optimize_suggestions(
            requirements.development_patterns,
            requirements.team_collaboration,
            productivity_docs
        )
        
        # Analyze productivity patterns
        productivity_analysis = self.productivity_analyzer.analyze_patterns(
            requirements.current_workflows,
            requirements.productivity_goals,
            ai_docs
        )
        
        return ProactiveSuggestionsArchitecture(
            suggestion_engine=suggestion_configuration,
            context_analysis=self._design_context_analysis(requirements),
            workflow_optimization=productivity_analysis,
            learning_system=self._implement_learning_system(requirements),
            integration_framework=self._design_integration_framework(requirements),
            monitoring_dashboard=self._create_monitoring_dashboard()
        )

// Enterprise proactive suggestions with TypeScript
interface SuggestionContext {
  userId: string;
  projectType: string;
  currentActivity: string;
  codebaseContext: CodebaseContext;
  teamContext: TeamContext;
  performanceMetrics: PerformanceMetrics;
}
interface Suggestion {
  id: string;
  type: SuggestionType;
  title: string;
  description: string;
  priority: Priority;
  actionability: Actionability;
  context: SuggestionContext;
  implementation?: ImplementationGuide;
  confidence: number;
  timestamp: Date;
}
export class ProactiveSuggestionEngine {
  private contextAnalyzer: ContextAnalyzer;
  private patternRecognizer: PatternRecognizer;
  private learningAdaptation: LearningAdaptation;
  private suggestionHistory: Map<string, Suggestion[]> = new Map();
  constructor() {
    this.contextAnalyzer = new ContextAnalyzer();
    this.patternRecognizer = new PatternRecognizer();
    this.learningAdaptation = new LearningAdaptation();
  }
  async generateSuggestions(context: SuggestionContext): Promise<Suggestion[]> {
    try {
      // Analyze current context
      const contextAnalysis = await this.contextAnalyzer.analyzeContext(context);
      
      // Recognize patterns
      const patterns = await this.patternRecognizer.recognizePatterns(
        context.codebaseContext,
        context.currentActivity
      );
      
      // Generate suggestions based on analysis
      const suggestions = await this.generateSuggestionsFromAnalysis(
        contextAnalysis,
        patterns,
        context
      );
      
      // Apply learning adaptation
      const adaptedSuggestions = await this.learningAdaptation.adaptSuggestions(
        suggestions,
        context.userId,
        context.teamContext
      );
      
      // Filter and prioritize suggestions
      const prioritizedSuggestions = this.prioritizeSuggestions(adaptedSuggestions);
      
      // Store suggestion history
      this.storeSuggestionHistory(context.userId, prioritizedSuggestions);
      
      return prioritizedSuggestions;
    } catch (error) {
      console.error('Error generating suggestions:', error);
      return [];
    }
  }
  private async generateSuggestionsFromAnalysis(
    contextAnalysis: ContextAnalysis,
    patterns: Pattern[],
    context: SuggestionContext
  ): Promise<Suggestion[]> {
    const suggestions: Suggestion[] = [];
    // Code quality suggestions
    const codeSuggestions = await this.generateCodeQualitySuggestions(
      contextAnalysis.codeAnalysis,
      patterns
    );
    suggestions.push(...codeSuggestions);
    // Performance optimization suggestions
    const performanceSuggestions = await this.generatePerformanceSuggestions(
      contextAnalysis.performanceAnalysis,
      context.performanceMetrics
    );
    suggestions.push(...performanceSuggestions);
    // Workflow optimization suggestions
    const workflowSuggestions = await this.generateWorkflowSuggestions(
      contextAnalysis.workflowAnalysis,
      context.teamContext
    );
    suggestions.push(...workflowSuggestions);
    // Learning and development suggestions
    const learningSuggestions = await this.generateLearningSuggestions(
      contextAnalysis.skillGapAnalysis,
      patterns
    );
    suggestions.push(...learningSuggestions);
    return suggestions;
  }
  private async generateCodeQualitySuggestions(
    codeAnalysis: CodeAnalysis,
    patterns: Pattern[]
  ): Promise<Suggestion[]> {
    const suggestions: Suggestion[] = [];
    // Check for code smells
    const codeSmells = await this.detectCodeSmells(codeAnalysis);
    for (const smell of codeSmells) {
      suggestions.push(this.createCodeSmellSuggestion(smell));
    }
    // Suggest refactoring opportunities
    const refactoringOps = await this.identifyRefactoringOpportunities(codeAnalysis);
    for (const opportunity of refactoringOps) {
      suggestions.push(this.createRefactoringSuggestion(opportunity));
    }
    // Suggest test improvements
    const testSuggestions = await this.generateTestSuggestions(codeAnalysis);
    suggestions.push(...testSuggestions);
    return suggestions;
  }
  private createCodeSmellSuggestion(codeSmell: CodeSmell): Suggestion {
    return {
      id: `code-smell-${Date.now()}`,
      type: 'CODE_QUALITY',
      title: `Code Smell Detected: ${codeSmell.type}`,
      description: `Found ${codeSmell.type} in ${codeSmell.location}. ${codeSmell.description}`,
      priority: codeSmell.severity === 'high' ? 'HIGH' : 'MEDIUM',
      actionability: 'IMMEDIATE',
      context: codeSmell.context,
      implementation: {
        steps: [
          `Review the ${codeSmell.type} in ${codeSmell.location}`,
          `Apply the recommended refactoring pattern`,
          `Run tests to ensure no regression`,
          `Consider adding unit tests for the refactored code`
        ],
        codeExample: codeSmell.example,
        references: codeSmell.references
      },
      confidence: 0.8,
      timestamp: new Date()
    };
  }
  private createRefactoringSuggestion(opportunity: RefactoringOpportunity): Suggestion {
    return {
      id: `refactor-${Date.now()}`,
      type: 'REFACTORING',
      title: `Refactoring Opportunity: ${opportunity.type}`,
      description: opportunity.description,
      priority: opportunity.impact === 'high' ? 'HIGH' : 'MEDIUM',
      actionability: 'PLANNED',
      context: opportunity.context,
      implementation: {
        steps: opportunity.steps,
        codeExample: opportunity.example,
        estimatedEffort: opportunity.effort,
        expectedBenefits: opportunity.benefits
      },
      confidence: 0.7,
      timestamp: new Date()
    };
  }
  private prioritizeSuggestions(suggestions: Suggestion[]): Suggestion[] {
    return suggestions.sort((a, b) => {
      // Sort by priority first
      const priorityOrder = { 'CRITICAL': 4, 'HIGH': 3, 'MEDIUM': 2, 'LOW': 1 };
      const priorityDiff = priorityOrder[b.priority] - priorityOrder[a.priority];
      
      if (priorityDiff !== 0) return priorityDiff;
      
      // Then sort by actionability
      const actionabilityOrder = { 'IMMEDIATE': 3, 'PLANNED': 2, 'FUTURE': 1 };
      const actionabilityDiff = actionabilityOrder[b.actionability] - actionabilityOrder[a.actionability];
      
      if (actionabilityDiff !== 0) return actionabilityDiff;
      
      // Finally sort by confidence
      return b.confidence - a.confidence;
    });
  }