Optimizes LLM API costs and performance by implementing provider-native prompt caching for Claude and OpenAI.
The Prompt Caching skill provides a production-ready framework for implementing native prompt caching across major LLM providers, including Anthropic's Claude and OpenAI's latest models. By strategically placing cache breakpoints in system prompts, few-shot examples, and large context blocks, this skill enables developers to reduce input token costs by up to 90% on repeated requests. It includes standardized Python implementations for managing ephemeral cache TTLs, calculating break-even points for cache writes, and monitoring hit rates to ensure maximum efficiency in high-volume AI applications.
Key Features
01Optimized prompt structuring to prevent accidental cache invalidation.
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03Support for 5-minute (1.25x write) and 1-hour (2x write) ephemeral TTL options.
04Built-in monitoring tools for cache hit rates and token savings tracking.
05Strategic cache breakpoint placement for system prompts and few-shot examples.
06Provider-native caching for Claude 3/4 and OpenAI GPT-5/o3 models.
Use Cases
01Accelerating high-volume few-shot prompting workflows by caching static example sets.
02Optimizing RAG applications that reuse massive context windows across multiple user queries.
03Reducing costs for chatbots with extensive system instructions or persona definitions.
