claude-usage-tracker/dist/optimization-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 ConversationCluster {
    id: string;
    type: "coding" | "analysis" | "writing" | "debugging" | "other";
    conversations: Array<{
        conversationId: string;
        cost: number;
        tokens: number;
        duration: number;
        efficiency: number;
    }>;
    avgCost: number;
    avgTokens: number;
    avgEfficiency: number;
    optimizationPotential: number;
    recommendations: string[];
}
export interface BatchProcessingOpportunity {
    conversationId: string;
    currentCost: number;
    batchCost: number;
    savings: number;
    eligibilityScore: number;
    reasoning: string;
    timeToProcess: number;
}
export interface ModelSwitchingRecommendation {
    conversationId: string;
    currentModel: string;
    recommendedModel: string;
    currentCost: number;
    projectedCost: number;
    savings: number;
    confidence: number;
    riskLevel: "low" | "medium" | "high";
    reasoning: string;
}
export interface OptimizationSummary {
    totalPotentialSavings: number;
    batchProcessingSavings: number;
    modelSwitchingSavings: number;
    efficiencyImprovements: number;
    recommendations: Array<{
        type: "batch" | "model_switch" | "efficiency";
        description: string;
        savings: number;
        effort: "low" | "medium" | "high";
    }>;
}
export declare class OptimizationAnalyzer {
    private readonly BATCH_API_DISCOUNT;
    private readonly MIN_BATCH_COST;
    clusterConversations(entries: UsageEntry[]): ConversationCluster[];
    identifyBatchProcessingOpportunities(entries: UsageEntry[]): {
        opportunities: BatchProcessingOpportunity[];
        totalPotentialSavings: number;
    };
    generateModelSwitchingRecommendations(entries: UsageEntry[]): {
        recommendations: ModelSwitchingRecommendation[];
        totalPotentialSavings: number;
    };
    generateOptimizationSummary(entries: UsageEntry[]): OptimizationSummary;
    private groupByConversation;
    private classifyConversationType;
    private calculateConversationDuration;
    private calculateClusterStats;
    private generateClusterRecommendations;
    private calculateBatchEligibility;
    private calculateAvgTimeBetweenMessages;
    private getBatchEligibilityReasoning;
    private estimateBatchProcessingTime;
    private analyzeModelSwitch;
    private assessConversationComplexity;
    private detectCodeContext;
    analyzeConversationClusters(entries: UsageEntry[]): {
        clusters: {
            conversationIds: string[];
            characteristics: {
                avgTokens: number;
                avgCost: number;
                complexity: number;
            };
            optimization: {
                potentialSavings: number;
                recommendation: string;
            };
        }[];
        totalConversations: number;
        avgClusterSize: number;
    };
    private removeBatchOverlaps;
    private calculateComplexityScore;
}
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