What problem does it solve? LLM applications repeatedly send identical system prompts and context, wasting tokens, increasing latency, and inflating API costs. This Skill provides concrete caching patterns to eliminate redundant token processing and serve repeated queries faster. ## Core Features & Use Cases - Anthropic Prompt Caching: Apply cache_control markers to stable system prompts and knowledge bases for up to 90% cost reduction on cached tokens. - Response Caching: Cache full LLM responses in Redis using exact-match hashing or semantic similarity, with temperature-aware rules. - Cache Augmented Generation (CAG): Pre-cache stable document corpora directly in the prompt as an alternative to RAG when the corpus fits in context. - Use Case: A support chatbot sends the same 50K-token documentation with every request. By marking it with cache_control, each subsequent call reads from cache instead of reprocessing, cutting cost and latency dramatically. ## Quick Start Ask the AI to add Anthropic prompt caching to your Claude API calls so the static system prompt and knowledge base are cached across requests.