Caches prompts and responses to reduce LLM costs and latency.
npx skills add https://github.com/zzafergok/skills --skill prompt-caching-zzafergok
Please help me install this Agent Skill. Skill: prompt-caching Source: https://github.com/zzafergok/skills/tree/main/01-ai-intelligence/prompt-caching Command: npx skills add https://github.com/zzafergok/skills --skill prompt-caching-zzafergok
Caching prompts and responses for large language models to reduce latency and operational costs while maintaining quality and relevance.
Activate a prompt and response caching workflow to reduce LLM costs in repeated queries.
High-intent search queries and answers about installing and using this skill.
Full-response caching stores identical or similar queries to serve them directly without recomputation, while prompt-prefix caching reuses common prompts to reduce parsing overhead and lower operational costs.
Prompt caching should be used in high-traffic chat systems or repetitive prompt-driven tasks like documentation generation, allowing you to cache frequent intents and minimize recomputation.
You implement cache invalidation for LLM prompts using built-in invalidation strategies that prevent stale results and ensure freshness, while safely handling context windows to maintain quality and relevance.
Yes, prompt caching safely handles context window limits by managing how cached prefixes and full responses are stored and retrieved, ensuring context boundaries are respected during cache invalidation.
Prefix caching reuses common prompt prefixes to reduce parsing overhead, while full-response caching stores entire identical or similar query outputs to serve them faster and cheaper without recomputation.
DhumitechCache prompts and responses in LLM workflows to reduce latency and API costs.
Prathmesh2000Analyze and estimate token usage for LLM API calls.
Manvendra08Reduce token consumption and context overflow in LLM interactions.
TwuanMinnImplement multi-level prompt caching for prefixes, responses, and semantic matches.
