token-cost-analyzer

Name: token-cost-analyzer
Availability: InStock
Author: latestaiagents

Analyze LLM API token usage and provide cost optimization strategies.

5|1|Updated Feb 4, 2026

Search Tags:#prompt engineering #llm #cost optimization #token usage #api spending #ai budget

Ready to run

npx skills add https://github.com/latestaiagents/agent-skills --skill token-cost-analyzer

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: token-cost-analyzer
Source: https://github.com/latestaiagents/agent-skills/tree/main/skills/mlops/llmops-guardian/token-cost-analyzer
Command: npx skills add https://github.com/latestaiagents/agent-skills --skill token-cost-analyzer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps users understand, track, and reduce their spending on Large Language Model (LLM) APIs by analyzing token usage and providing optimization strategies.

Core Features & Use Cases

Cost Auditing: Analyze historical API costs, breaking them down by model, operation, and time.
Optimization Strategies: Provides guidance on selecting cost-effective models, optimizing prompts, controlling response length, and leveraging caching.
Use Case: A development team notices a surge in their AI service costs. They use this skill to identify which operations are most expensive and which models are being overused for simple tasks, then implement prompt optimizations to reduce token consumption.

Quick Start

Analyze the token costs for the last month to identify the most expensive operations.

Frequently Asked Questions about token-cost-analyzer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I optimize LLM API spending and reduce token usage?▼

To optimize LLM API spending, you can audit historical token usage to identify expensive operations, apply prompt optimization techniques, select cost-effective models, and leverage caching to control response length and overall AI budget.

What is the best way to audit AI service expenditure by model and operation?▼

Auditing AI service expenditure involves analyzing historical API costs to break down token usage by specific model, operation, and time period, which identifies overused models for simple tasks and highlights operations requiring prompt optimization.

How do I analyze token costs to identify which LLM operations are most expensive?▼

Analyzing token costs requires breaking down historical API usage data to isolate which operations consume the most tokens, allowing you to pinpoint expensive processes and implement actionable cost optimization strategies like prompt adjustments.

When do I need prompt engineering for cost efficiency in LLM applications?▼

Prompt engineering for cost efficiency is needed when API spending surges or when simple tasks are routed to expensive models, requiring prompt optimization and model selection guidance to manage the AI budget without losing functionality.

Can I use token analysis to control response length and manage my AI budget?▼

Yes, token analysis allows you to control response length and manage AI budgets by auditing token usage patterns, providing actionable strategies to reduce consumption through caching, prompt optimization, and selecting cost-effective LLM models.