claude-usage-tracker/dist/research-analytics.d.ts

Advanced analytics for Claude Code usage with cost optimization, conversation length analysis, and rate limit tracking

import type { UsageEntry } from "./types.js";
export interface ConversationSuccessMetrics {
    conversationId: string;
    messageCount: number;
    totalCost: number;
    totalTokens: number;
    duration: number;
    successScore: number;
    efficiency: number;
    promptComplexity: number;
    cacheUtilization: number;
    modelSwitches: number;
    timeOfDay: number;
    dayOfWeek: number;
    avgResponseTime: number;
}
export interface ProjectAnalysis {
    projectPath: string;
    totalCost: number;
    totalTokens: number;
    conversationCount: number;
    avgCostPerConversation: number;
    efficiency: number;
    timeSpent: number;
    roi: number;
    primaryModel: string;
    topics: string[];
}
export interface TimeSeriesDataPoint {
    date: string;
    dailyCost: number;
    dailyTokens: number;
    conversationCount: number;
    avgEfficiency: number;
    opusPercentage: number;
    cacheHitRate: number;
}
export interface CacheOptimizationInsight {
    totalCacheTokens: number;
    cacheHitRate: number;
    cacheSavings: number;
    underutilizedConversations: Array<{
        conversationId: string;
        missedCachingOpportunity: number;
    }>;
    recommendations: string[];
}
export interface PromptingPatternAnalysis {
    avgPromptLength: number;
    effectivePromptPatterns: Array<{
        pattern: string;
        successRate: number;
        efficiency: number;
        examples: string[];
    }>;
    inefficientPatterns: Array<{
        pattern: string;
        wasteRate: number;
        avgCost: number;
    }>;
    optimalPromptingGuidelines: string[];
}
export interface AdvancedInsights {
    conversationSuccess: ConversationSuccessMetrics[];
    projectAnalysis: ProjectAnalysis[];
    timeSeriesData: TimeSeriesDataPoint[];
    cacheOptimization: CacheOptimizationInsight;
    promptingPatterns: PromptingPatternAnalysis;
    correlationInsights: Array<{
        factor1: string;
        factor2: string;
        correlation: number;
        insight: string;
    }>;
}
export declare class ResearchAnalyzer {
    analyzeConversationSuccess(entries: UsageEntry[]): ConversationSuccessMetrics[];
    analyzeProjectROI(entries: UsageEntry[]): ProjectAnalysis[];
    generateTimeSeriesData(entries: UsageEntry[]): TimeSeriesDataPoint[];
    analyzeCacheOptimization(entries: UsageEntry[]): CacheOptimizationInsight;
    analyzePromptingPatterns(entries: UsageEntry[]): PromptingPatternAnalysis;
    generateAdvancedInsights(entries: UsageEntry[]): AdvancedInsights;
    private groupByConversation;
    private calculateSuccessScore;
    private calculatePromptComplexity;
    private calculateCacheUtilization;
    private countModelSwitches;
    private calculateAvgResponseTime;
    private extractProjectPath;
    private calculateProjectTimeSpent;
    private calculateROI;
    private getPrimaryModel;
    private inferTopics;
    private calculateDailyCacheHitRate;
    private calculateMissedCachingOpportunity;
    private generateCacheRecommendations;
    private generatePromptingGuidelines;
    private calculateCorrelationInsights;
    private calculateCorrelation;
    calculateProjectROI(entries: UsageEntry[]): {
        projects: {
            projectId: string;
            totalCost: number;
            conversationCount: number;
            roi: number;
            efficiency: number;
            avgCostPerConversation: number;
            roiScore: number;
            characteristics: string[];
            recommendations: string[];
        }[];
        totalInvestment: number;
        avgROI: number;
        insights: {
            topPerformers: {
                projectId: string;
                totalCost: number;
                conversationCount: number;
                roi: number;
                efficiency: number;
                avgCostPerConversation: number;
                roiScore: number;
                characteristics: string[];
                recommendations: string[];
            }[];
            underperformers: {
                projectId: string;
                totalCost: number;
                conversationCount: number;
                roi: number;
                efficiency: number;
                avgCostPerConversation: number;
                roiScore: number;
                characteristics: string[];
                recommendations: string[];
            }[];
        };
        recommendations: string[];
    };
    findCorrelations(entries: UsageEntry[]): {
        correlations: {
            variables: string[];
            variable1: string;
            variable2: string;
            coefficient: number;
            strength: number;
            pValue: number;
            significance: string;
            interpretation: string;
            description: string;
        }[];
        strongestCorrelations: {
            variables: string[];
            variable1: string;
            variable2: string;
            coefficient: number;
            strength: number;
            pValue: number;
            significance: string;
            interpretation: string;
            description: string;
        }[];
        insights: string[];
    };
    analyzeConversationSuccessMetrics(entries: UsageEntry[]): {
        successMetrics: {
            totalConversations: number;
            completionRate: number;
            avgSuccessScore: number;
        };
        conversationCategories: {
            successful: string[];
            struggling: string[];
            abandoned: string[];
        };
        patterns: {
            successFactors: string[];
            commonFailurePoints: string[];
        };
        recommendations: string[];
    };
}
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