claude-usage-tracker/dist/pattern-analysis.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 ConversationLengthPattern {
    lengthCategory: "short" | "medium" | "long" | "extended";
    messageCount: number;
    avgTokensPerMessage: number;
    avgCostPerMessage: number;
    efficiency: number;
    frequency: number;
    trendDirection: "increasing" | "decreasing" | "stable";
}
export interface TimeToCompletionAnalysis {
    taskType: string;
    avgCompletionTime: number;
    medianCompletionTime: number;
    successRate: number;
    efficiencyTrend: "improving" | "declining" | "stable";
    optimalSessionLength: number;
    recommendations: string[];
}
export interface TaskSwitchingPattern {
    switchFrequency: number;
    avgTimeBetweenSwitches: number;
    costOfSwitching: number;
    mostCommonTransitions: Array<{
        from: string;
        to: string;
        frequency: number;
        avgGapTime: number;
    }>;
    recommendations: string[];
}
export interface LearningCurveAnalysis {
    skillArea: string;
    initialEfficiency: number;
    currentEfficiency: number;
    improvementRate: number;
    plateauDetected: boolean;
    timeToCompetency: number;
    costToCompetency: number;
    learningPhase: "novice" | "developing" | "competent" | "expert";
    nextMilestone: string;
}
export interface UsagePattern {
    patternType: "peak_hours" | "model_preference" | "cost_sensitivity" | "efficiency_cycles";
    description: string;
    strength: number;
    impact: "low" | "medium" | "high";
    actionable: boolean;
    recommendation?: string;
}
export declare class PatternAnalyzer {
    analyzeConversationLengthPatterns(entries: UsageEntry[]): {
        conversationTypes: {
            quickQuestions: {
                count: number;
            };
            detailedDiscussions: {
                count: number;
            };
            deepDives: {
                count: number;
            };
        };
        avgLengthByType: {
            quickQuestions: number;
            detailedDiscussions: number;
            deepDives: number;
        };
        costDistribution: {
            quickQuestions: {
                avgCost: number;
                totalCost: number;
            };
            detailedDiscussions: {
                avgCost: number;
                totalCost: number;
            };
            deepDives: {
                avgCost: number;
                totalCost: number;
            };
        };
        efficiencyInsights: {
            mostEfficientType: string;
            leastEfficientType: string;
            avgTokensPerExchange: number;
        };
        recommendations: string[];
    };
    analyzeTimeToCompletion(entries: UsageEntry[]): TimeToCompletionAnalysis[];
    analyzeTaskSwitchingPatterns(entries: UsageEntry[]): TaskSwitchingPattern;
    analyzeLearningCurve(entries: UsageEntry[], skillArea?: string): LearningCurveAnalysis;
    identifyUsagePatterns(entries: UsageEntry[]): UsagePattern[];
    private groupByConversation;
    private categorizeConversationLength;
    private calculateLengthTrend;
    private inferTaskType;
    private calculateCompletionTime;
    private inferTaskSuccess;
    private calculateMedian;
    private calculateEfficiencyTrend;
    private findOptimalSessionLength;
    private generateCompletionRecommendations;
    private estimateSwitchingCost;
    private generateSwitchingRecommendations;
    private filterBySkillArea;
    private createDefaultLearningAnalysis;
    private calculateRecentEfficiencyTrend;
    private determineLearningPhase;
    private estimateTimeToCompetency;
    private estimateCostToCompetency;
    private suggestNextMilestone;
    private analyzeHourlyDistribution;
    private detectPeakHoursPattern;
    private analyzeModelPreference;
    private analyzeCostSensitivity;
    private detectEfficiencyCycles;
    private getDailyCosts;
    private calculateStandardDeviation;
    identifyLearningCurves(entries: UsageEntry[]): {
        periods: {
            startDate: string;
            endDate: string;
            metrics: {
                avgQuestionsPerDay: number;
                avgComplexityScore: number;
            };
            characteristics: string[];
        }[];
        overallTrend: "stable" | "improving" | "declining";
        insights: string[];
    };
    private calculateAverageComplexity;
    private classifyTaskType;
    private groupTasksByType;
    private calculateLongestFocusedSession;
}
//# sourceMappingURL=pattern-analysis.d.ts.map