redis-vector-search

Optimizes Redis performance by providing comprehensive guidelines for data structures, caching, vector search, and query engine best practices across 29 rules.

Core Redis data modeling guidance for choosing the right data structure (String, Hash, List, Set, Sorted Set, JSON, Stream, Vector Set) and using consistent colon-separated key naming conventions.

An agent skill that teaches best practices for Redis connection management, covering connection pooling, multiplexing, pipelining, client-side caching, timeouts, and slow command handling.

An agent skill that teaches Redis security best practices, covering authentication and TLS, ACL-based least-privilege access, network binding, firewall configuration, and command renaming.

A collection of Redis agent skills covering core data structures, connection management, query engine, vector search, semantic caching, clustering, security, observability, and the Iris agent memory system. Installable via the Agent Skills CLI, Claude Code plugin, or Cursor plugin.

A collection of Redis agent skills covering core data structures, connection management, query engine (FT.CREATE/FT.SEARCH), vector search, semantic caching, clustering, security, observability, and the Iris agent memory system.

An AI agent skill for implementing Redis-based semantic caching (LangCache) for LLM responses, covering cache-aside flow, similarity threshold tuning, and per-task cache separation. Part of the Redis agent skills collection.

An AI agent skill for Redis clustering that teaches agents how to use hash tags for multi-key operations, avoid CROSSSLOT errors, and read from replicas. Part of the Redis agent skills collection for coding assistants.

A Redis agent skill for building with Iris (Redis Agent Memory), covering SDK authentication, session event tracking, long-term memory search, and async memory promotion for AI agent applications.

Skill