redis/agent-skills
7 skills7.6k installs658 starsGitHub
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npx skills add https://github.com/redis/agent-skillsSkills in this repo
1Redis CoreThe redis-core skill provides foundational Redis modeling guidance for choosing among String, Hash, List, Set, Sorted Set, JSON, Stream, and Vector Set structures plus consistent colon-separated key naming. It helps agents design keys and structures that scale without hot-key anti-patterns or ambiguous namespaces. Patterns cover caching, leaderboards, queues, pub/sub, and vector search use cases with structure-specific tradeoffs. Agents apply redis-core before implementing Redis clients in application backends. Redis structure selection: String, Hash, List, Set, ZSet, JSON, Stream, Vector. Colon-separated consistent key naming conventions. Caching, queues, leaderboards, and vector search patterns. Scalable data model guidance without hot-key pitfalls. Foundational Redis modeling before client implementation. Choose Redis data structures and colon-separated key naming for scalable data models.1.7kinstalls2Redis ConnectionsRedis Connections teaches client-side efficiency patterns for redis-py, Jedis, Lettuce, go-redis, and NRedisStack across five core areas. It forbids per-request TCP connections in favor of connection pools or multiplexed single connections, noting multiplexed clients cannot carry blocking commands like BLPOP. Pipelining batches independent commands into one round trip, with transactional pipelines reserved for true atomicity needs. Production guidance replaces KEYS, SMEMBERS, HGETALL, and full LRANGE with SCAN-family cursor loops and pagination. RESP3 client-side caching suits read-heavy rarely-written data such as config and feature flags, while explicit socket connect and read timeouts enable fast failure without breaking healthy traffic.1.2kinstalls3Redis SecurityThe redis-security skill covers production Redis hardening across authentication, ACL-based access control, and network exposure together because any single control leaves gaps. It documents requirepass plus TLS port configuration, Python ssl client examples, per-application ACL users with command and key-pattern restrictions, bind and protected-mode guidance, firewall rules, and disabling dangerous commands. Use when deploying Redis to production, defining app credentials, configuring TLS, locking instances behind firewalls, or remediating internet-exposure scanner findings. References expand auth patterns and ACL recipes for read-only, writer, and admin roles.1.1kinstalls4Redis ObservabilityRedis observability guidance — which metrics to monitor (memory, connections, hit ratio, ops/sec, rejected connections), which built-in commands to reach for during incident triage (SLOWLOG, INFO, MEMORY DOCTOR, CLIENT LIST, FT.PROFILE), and when to use the Redis Insight GUI. Use when setting up monitoring or alerts for a Redis instance, diagnosing a performance regression, profiling a slow FT.SEARCH query, or wiring Redis metrics into Prometheus, Datadog, or similar. What to watch, what to run, and what to alert on. Covers the metrics every Redis deployment should monitor and the built-in commands for ad-hoc diagnosis.1kinstalls5Redis Semantic CacheRedis LangCache guidance for semantic caching of LLM responses on Redis Cloud — calling search/set via the SDK or REST API, tuning the similarity threshold, separating caches per task type, and filtering with custom attributes. Use when caching LLM completions or RAG answers to cut API cost and latency, building a cache-aside layer in front of OpenAI / Anthropic / etc., tuning hit rate vs precision, or splitting one app's LLM workloads into multiple LangCache caches. Semantic caching for LLM responses with Redis Cloud's LangCache service. Stores prompts as embeddings; subsequent semantically-similar prompts return the cached response without re-calling the model.928installs6Redis ClusteringRedis Cluster and replication guidance covering hash tags for multi-key operations, avoiding CROSSSLOT errors, and reading from replicas to scale read-heavy workloads. Use when designing keys for a sharded Redis Cluster, debugging CROSSSLOT errors on MGET / SDIFF / pipelines, configuring a multi-key transaction in a cluster, or routing reads to replicas for caches, analytics, or dashboards. Guidance for designing keys and routing reads in a sharded Redis Cluster (and in standalone primary/replica replication). Covers the two failure modes that bite most new cluster users: `CROSSSLOT` errors on multi-key operations, and overloading primaries with read traffic.888installs7Iris DevelopmentIris is Redis's umbrella for AI-focused products. Use this skill when integrating with the Iris Redis Agent Memory (RAM) data plane on Redis Cloud — recording session events for an AI agent, creating or searching long-term memories, configuring a memory store, or tuning background memory promotion. Code examples use the official `redis-agent-memory` (Python) and `@redis-iris/agent-memory` (TypeScript) SDKs. **Iris** is the umbrella brand for Redis's AI-focused products. This skill currently covers one product in that family: **Redis Agent Memory (RAM)** — the persistent memory layer for AI agents, delivered as a managed service on Redis Cloud. Additional Iris products will be added as separate sections when they ship.831installs