Individual skill
implementing-database-caching
Process use when you need to implement multi-tier caching to improve database performance. This skill sets up Redis, in-memory caching, and CDN layers to reduce database load. Trigger with phrases like "implement database caching", "add Redis cache layer", "improve query performance with caching", or "reduce database load".
plugins/database/database-cache-layer/skills/implementing-database-caching/SKILL.md
Source commit 0500b275e963c027f6b410e8c49c0446945c574e
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- Last checked
- Nov 12, 2026
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- 2,915
- Repository last updated
- Nov 12, 2026
- License
- MIT
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- ai Repository topic
- automation Repository topic
- claude-code Repository topic
- devops Repository topic
- mcp Repository topic
- ai-agents Repository topic
- anthropic Repository topic
- claude-code-plugins Repository topic
- developer-tools Repository topic
- llm Repository topic
- database Declared skill tag
- redis Declared skill tag
- performance Declared skill tag
- data Derived category
- data-databases Derived category
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SKILL.mdcore · 7,388 bytesassets/README.mddoc · 136 bytesreferences/README.mddoc · 63 bytesscripts/README.mddoc · 339 bytesscripts/redis_setup.pyruns · 2,513 bytes
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# Database Cache Layer
## Overview
Implement multi-tier caching strategies using Redis, application-level in-memory caches, and query result caching to reduce database load and improve read latency. This skill covers cache-aside, write-through, and write-behind patterns with proper invalidation strategies, TTL configuration, and cache stampede prevention.
## Prerequisites
- Redis server (6.x+) available or Docker for running `docker run redis:7-alpine`
- `redis-cli` installed for cache inspection and debugging
- Application framework with Redis client library (ioredis, redis-py, Jedis, go-redis)
- Database query profiling data identifying read-heavy and slow queries
- Understanding of data freshness requirements (how stale can cached data be)
- Monitoring tools for cache hit rate and Redis memory usage
