Design Redis deployments covering data modeling, TTL, persistence, HA, and security.
npx skills add https://github.com/westonwrz/codex-skills-by-codex --skill redis-westonwrz
Please help me install this Agent Skill. Skill: redis Source: https://github.com/westonwrz/codex-skills-by-codex/tree/main/redis Command: npx skills add https://github.com/westonwrz/codex-skills-by-codex --skill redis-westonwrz
This Skill helps you design, implement, and operate Redis (6+) safely and efficiently, avoiding common pitfalls and ensuring optimal performance and reliability.
Use the redis skill to design a Redis plan including keys, TTLs, eviction, persistence, and HA, and identify performance and security risks.
High-intent search queries and answers about installing and using this skill.
Design Redis caching by selecting appropriate eviction policies, modeling data structures efficiently, and setting TTLs to manage memory. This ensures optimal performance and automatic data eviction when capacity is reached.
Configure Redis high availability by choosing Sentinel for automatic failover with replication, or Cluster for horizontal scaling across multiple nodes. Sentinel suits single-master setups, while Cluster distributes data across shards for higher throughput.
Debug Redis performance issues by analyzing slowlog to identify expensive commands and monitoring latency events. Inspect blocking commands and anti-patterns to resolve bottlenecks and restore efficient data operations.
Secure Redis 6+ by configuring Access Control Lists to restrict commands per user and enabling TLS for network encryption. This protects data in transit and limits unauthorized access to sensitive cache and session operations.
Choose between RDB and AOF persistence by weighing RDB's faster startup and compact snapshots against AOF's finer-grained durability. AOF logs every write operation, reducing data loss risk during failures compared to RDB.
Avoid Redis anti-patterns like using blocking commands that stall throughput, oversized keys that cause latency spikes, and lacking memory bounds for rate-limiting or queue operations. Proper data modeling and TTL strategies prevent these bottlenecks.
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