Implement three-layer LLM caching with Redis deduplication and Anthropic prompt caching.
npx skills add https://github.com/teodorboev/socialai --skill llm-caching
Please help me install this Agent Skill. Skill: llm-caching Source: https://github.com/teodorboev/socialai/tree/main/.opencode/skills/llm-caching Command: npx skills add https://github.com/teodorboev/socialai --skill llm-caching
This Skill addresses the high cost of Large Language Model (LLM) usage by implementing a multi-layered caching system, significantly reducing operational expenses for AI-driven applications.
Implement Layer 1 by adding Upstash Redis and modifying the engagement monitor to call the checkEngagementChanged function before dispatching to the engagement agent.
High-intent search queries and answers about installing and using this skill.
Reduce LLM API costs by implementing a three-layer caching system: Redis engagement deduplication, Anthropic prompt caching via cache_control headers, and template short-circuiting for predictable responses. This approach targets significant cost reduction for high-frequency AI agents and content pipelines.
Anthropic prompt caching lowers token usage by caching static prompt parts via cache_control headers, charging only for variable tokens. Repeated API calls with identical system instructions or context avoid reprocessing the entire prompt, significantly reducing operational expenses.
Yes, Redis is required for the engagement scan deduplication layer, which prevents redundant LLM calls for unchanged social media activity. You add Upstash Redis and call the checkEngagementChanged function before dispatching to the engagement agent.
The best way to bypass LLM calls for predictable responses is template short-circuiting, which uses a keyword classifier and template pool to return responses without invoking the model. This avoids token costs entirely for low-value, highly predictable outputs.
Yes, prompt caching and engagement deduplication are designed for social media management platforms processing many posts. By preventing redundant calls for unchanged activity and caching static prompt components, a platform can reduce its monthly AI bill by approximately 50%.
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