implementing-database-caching
About
This skill enables Claude to implement multi-tier database caching solutions using Redis, in-memory caches, and CDNs to reduce database load and improve query latency. It activates when users request performance improvements, database caching, or use the `/caching` command. The skill supports patterns like cache-aside and write-through for horizontal scaling.
Quick Install
Claude Code
Recommended/plugin add https://github.com/jeremylongshore/claude-code-plugins-plusgit clone https://github.com/jeremylongshore/claude-code-plugins-plus.git ~/.claude/skills/implementing-database-cachingCopy and paste this command in Claude Code to install this skill
Documentation
Overview
This skill empowers Claude to implement a production-ready multi-tier caching architecture for databases. It leverages Redis for distributed caching, in-memory caching for L1 performance, and CDN for static assets. This results in significant database load reduction, improved query latency, and enhanced scalability.
How It Works
- Identify Caching Requirements: Claude analyzes the user's request to determine specific caching needs and database technologies in use.
- Implement Caching Layers: Claude generates code to implement Redis caching, in-memory caching, and CDN integration based on identified requirements.
- Configure Caching Strategies: Claude sets up appropriate caching strategies such as cache-aside, write-through, or read-through to optimize performance and data consistency.
When to Use This Skill
This skill activates when you need to:
- Implement a caching layer for a database.
- Improve database query performance.
- Reduce database load.
Examples
Example 1: Implementing Redis Caching
User request: "Implement Redis caching for my PostgreSQL database to improve query performance."
The skill will:
- Generate code to integrate Redis as a caching layer for the PostgreSQL database.
- Configure cache-aside strategy for frequently accessed data.
Example 2: Adding In-Memory Caching
User request: "Add an in-memory cache layer to my application to reduce latency for frequently accessed data."
The skill will:
- Implement an in-memory cache using a suitable library (e.g.,
lru-cacheor similar). - Configure the application to check the in-memory cache before querying the database.
Best Practices
- Cache Invalidation: Implement proper cache invalidation strategies to ensure data consistency.
- Cache Key Design: Design effective cache keys to avoid collisions and maximize cache hit rate.
- Monitoring: Monitor cache performance and adjust caching strategies as needed.
Integration
This skill can be integrated with other database management and deployment tools to automate the entire caching implementation process. It also complements skills related to database schema design and query optimization.
GitHub Repository
Related Skills
sglang
MetaSGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.
evaluating-llms-harness
TestingThis Claude Skill runs the lm-evaluation-harness to benchmark LLMs across 60+ standardized academic tasks like MMLU and GSM8K. It's designed for developers to compare model quality, track training progress, or report academic results. The tool supports various backends including HuggingFace and vLLM models.
llamaguard
OtherLlamaGuard is Meta's 7-8B parameter model for moderating LLM inputs and outputs across six safety categories like violence and hate speech. It offers 94-95% accuracy and can be deployed using vLLM, Hugging Face, or Amazon SageMaker. Use this skill to easily integrate content filtering and safety guardrails into your AI applications.
langchain
MetaLangChain is a framework for building LLM applications using agents, chains, and RAG pipelines. It supports multiple LLM providers, offers 500+ integrations, and includes features like tool calling and memory management. Use it for rapid prototyping and deploying production systems like chatbots, autonomous agents, and question-answering services.
