Provide in-memory key-value caching with TTL, LRU, and RESP protocol support.
npx skills add https://github.com/GuitarAlchemist/ix --skill ix-cache
Please help me install this Agent Skill. Skill: ix-cache Source: https://github.com/GuitarAlchemist/ix/tree/main/.claude/skills/ix-cache Command: npx skills add https://github.com/GuitarAlchemist/ix --skill ix-cache
ix-cache solves the problem of needing an efficient in-memory key-value store for applications that require caching, TTL-based expiry, LRU eviction, pub/sub messaging, or a lightweight Redis-compatible RESP server.
set, get, delete, keys operations.To get started, create an instance of the Cache and set a key with a TTL, then retrieve it:
use ix_cache::{Cache, CacheConfig};
let cache = Cache::new(CacheConfig::default());
cache.set("key", &json!("value"), Some(std::time::Duration::from_secs(10)));
if let Some(value) = cache.get("key") {
println!("Value retrieved: {:?}", value);
}
High-intent search queries and answers about installing and using this skill.
In-memory key-value caching with TTL is implemented using a concurrent sharded HashMap supporting set, get, delete, and keys operations, alongside automatic time-to-live expiry to maintain cache freshness without external infrastructure.
An in-process caching solution provides a Redis-compatible API and RESP protocol server, enabling fast, efficient data handling and reduced load times while requiring no external caching infrastructure to operate.
LRU eviction automatically removes the least recently used keys when the in-memory cache reaches its maximum capacity, ensuring memory constraints are respected while maintaining optimal application performance.
Yes, in-process publish/subscribe channels provide real-time pub/sub messaging capabilities, allowing components within the application to communicate efficiently without relying on an external message broker.
The in-memory store supports snapshotting to disk for data preservation and can automatically restore from these snapshots on startup, ensuring cached data survives application restarts.
An in-process cache is limited to the application's memory space and cannot be shared across multiple separate application instances, making it suitable for single-process performance enhancement rather than distributed caching.
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