Claude Code Cost Optimization

What problem does it solve?

This Skill helps users understand and reduce their costs associated with using Claude AI models by providing insights into token usage, model selection, and efficient prompting strategies.

Core Features & Use Cases

• Cost Tracking: Monitor input/output tokens, cache usage, and estimated costs via the /cost command.
• Model Management: Switch between different Claude models (Opus, Sonnet, Haiku) for optimal performance and cost-efficiency using /model commands or CLI overrides.
• Token Reduction: Implement strategies like using /compact, targeted file reads, sub-agents, and Grep to minimize token consumption.
• Caching: Maximize prompt and conversation caching for significant cost savings.
• Provider Options: Understand pricing differences and configuration for Anthropic Direct, AWS Bedrock, and Google Vertex AI.
• Batch Processing: Utilize the Message Batches API for up to 50% savings on non-interactive workloads.

Quick Start

Run the /cost command to see your current token consumption and estimated expenses.