llm-caching-patterns
CommunitySlash LLM costs with multi-level caching.
Authoryonatangross
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill reduces the cost and latency of large-language-model workflows by implementing a four-tier caching architecture that sits between user queries and the LLM, enabling reuse of exact and similar prompts and responses.
Core Features & Use Cases
- Multi-level caching: L1 in-memory exact-match, L2 Redis semantic cache, L3 provider-native prompt caching, L4 full LLM call with cost reporting.
- Cost optimization at scale: Dramatically lowers per-request cost in high-traffic environments, with observability and rollups via Langfuse.
- Real-world scenarios: Code review automation, chatbots, content moderation, or any repetitive query workloads across multiple agents.
Quick Start
Bootstrapping steps: install dependencies, initialize Redis, run the warming script, and validate via the provided observability dashboards.
Dependency Matrix
Required Modules
None requiredComponents
scriptsreferences
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: llm-caching-patterns Download link: https://github.com/yonatangross/create-yg-app/archive/main.zip#llm-caching-patterns Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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