Can I use this skill to justify AI costs to my stakeholders?

Absolutely. The skill includes ROI calculation templates that factor in manual labor costs, quality improvements, and token efficiency to show the true business value of your implementation.

What is the 'effort' parameter mentioned in the optimization guide?

The effort parameter is a setting for models like Opus 4.5 that controls reasoning depth, allowing you to choose between high, medium, and low settings to save 20-70% in tokens for simpler tasks.

How much impact does prompt caching have on costs?

Prompt caching can reduce costs by up to 90% for the cached portion of your input, which is particularly effective for large system prompts, documentation, or repeated files.

How does the Claude Cost Optimization skill help reduce expenses?

It identifies expensive token usage patterns and guides you through implementing caching, switching model tiers, and adjusting the 'effort' parameter to save up to 90% on your API bill.

Does this skill require specific API permissions?

Yes, to use the automated usage tracking and reporting features, you will need Anthropic Admin API access to retrieve your organization's metrics.

Claude Cost Optimization

Name: Claude Cost Optimization
Author: adaptationio

byadaptationio

分析と監視

Monitors and reduces Anthropic API expenses through advanced token tracking and implementation of optimization patterns like prompt caching and effort selection.

概要

This skill provides a comprehensive framework for managing production costs in Claude-based applications. It guides users through measuring baseline usage via the Admin API, identifying expensive interaction patterns, and implementing high-impact optimizations such as context editing, tool search, and the 'effort' parameter. By applying these patterns, developers can achieve up to 90% cost reduction on specific tasks, making it essential for teams looking to scale their AI integrations while maintaining high ROI and budget predictability.

主な機能

Context editing and client-side compaction for long conversation management
Implementation of the 'effort' parameter for 20-70% token reduction
Advanced tool optimization patterns including deferred loading and tool search
0 GitHub stars
Standardized ROI calculation templates for business value reporting
Admin API usage tracking and automated monthly cost forecasting

ユースケース

Reducing high monthly Claude API bills in production agent environments
Optimizing long-form research agents to prevent token bloat and context drift
Forecasting and planning budgets for scaling AI features to larger user bases

概要

主な機能

Context editing and client-side compaction for long conversation management
Implementation of the 'effort' parameter for 20-70% token reduction
Advanced tool optimization patterns including deferred loading and tool search
0 GitHub stars
Standardized ROI calculation templates for business value reporting
Admin API usage tracking and automated monthly cost forecasting

ユースケース

Reducing high monthly Claude API bills in production agent environments
Optimizing long-form research agents to prevent token bloat and context drift
Forecasting and planning budgets for scaling AI features to larger user bases