llm-caching

What problem does it solve?

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.

Core Features & Use Cases

• Engagement Scan Deduplication: Prevents redundant LLM calls for unchanged social media activity using Redis.
• Anthropic Prompt Caching: Reduces token costs by caching static parts of prompts, paying only for variable tokens.
• Template Short-Circuit: Bypasses LLM entirely for predictable, low-value responses using a keyword classifier and template pool.
• Use Case: A social media management platform can use this skill to reduce its monthly AI bill by approximately 50%, making its services more profitable and affordable for clients.

Quick Start

Implement Layer 1 by adding Upstash Redis and modifying the engagement monitor to call the checkEngagementChanged function before dispatching to the engagement agent.