What problem does it solve? Choosing and configuring an in-memory cache in Go involves hard decisions: which eviction algorithm fits the access pattern, how to size capacity from a memory budget, and how to avoid stale data, thundering herds, and runtime panics. This Skill guides those decisions for the samber/hot caching library. ## Core Features & Use Cases - Algorithm Selection: Choose among W-TinyLFU, LRU, LFU, TinyLFU, S3FIFO, ARC, TwoQueue, SIEVE, and FIFO based on measured access patterns, with a decision tree and comparison matrix. - Cache Construction: Build caches with TTL, jitter, janitor goroutines, read-through loaders with singleflight deduplication, copy-on-read/write protection, and missing-key (negative) caching. - Capacity Sizing & Monitoring: Derive capacity from a memory budget and measured item size, then track hit rate and eviction rate with Prometheus metrics. - Use Case: A Go service repeatedly loads the same user records from a database. Use this Skill to build a W-TinyLFU cache with a batch loader, TTL with jitter, sharding for concurrency, and Prometheus dashboards to validate hit rate above 80%. ## Quick Start Ask the AI to build a samber/hot cache for your Go service with a TTL, a database loader, and Prometheus metrics, sized for a given memory budget.