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Found 176 Skills
Redis 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.
Redis vector search guidance covering HNSW vs FLAT algorithm choice, vector index configuration (dims, distance metric, datatype), filtered hybrid search combining vector similarity with TAG or NUMERIC filters, and the RAG retrieval pattern with RedisVL. Use when defining a VECTOR field in FT.CREATE, integrating embeddings (OpenAI, Cohere, sentence-transformers), tuning HNSW parameters (M, EF_CONSTRUCTION, EF_RUNTIME), building a retrieval-augmented generation pipeline, or filtering vector results by attribute.
Iris 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.
Redis Query Engine (RQE) guidance covering FT.CREATE schema design, field type selection (TEXT, TAG, NUMERIC, GEO, GEOSHAPE, VECTOR), DIALECT 2 query syntax, efficient FT.SEARCH and FT.AGGREGATE queries, zero-downtime index updates via aliases, and the SKIPINITIALSCAN option. Use when defining a search index on Hash or JSON documents, picking between TEXT and TAG for filtering, writing FT.SEARCH queries with filters and SORTBY, managing or swapping indexes in production, or troubleshooting slow searches with FT.PROFILE.
Identify and quantify cost savings across Azure subscriptions by analyzing actual costs, utilization metrics, and generating actionable optimization recommendations. USE FOR: optimize Azure costs, reduce Azure spending, reduce Azure expenses, analyze Azure costs, find cost savings, generate cost optimization report, find orphaned resources, rightsize VMs, cost analysis, reduce waste, Azure spending analysis, find unused resources, optimize Redis costs. DO NOT USE FOR: deploying resources (use azure-deploy), general Azure diagnostics (use azure-diagnostics), security issues (use azure-security)
Vercel data and storage services including Postgres, Redis, Vercel Blob, Edge Config, and data cache. Use when selecting data storage or caching on Vercel.
Use when building real-time communication systems with WebSockets or Socket.IO. Invoke for bidirectional messaging, horizontal scaling with Redis, presence tracking, room management.
Implement query caching strategies to improve performance. Use when setting up caching layers, configuring Redis, or optimizing database query response times.
Distributed locking patterns with Redis and PostgreSQL for coordination across instances. Use when implementing exclusive access, preventing race conditions, or coordinating distributed resources.
Generate Go cache implementations following GO modular architechture conventions. Use when creating cache layers in internal/modules/<module>/cache/ - user state caching, session caching, rate limiting data, temporary data storage, or any domain cache that uses Redis for fast data access with TTL support.
BullMQ queue system reference for Redis-backed job queues, workers, flows, and schedulers. Use when: (1) creating queues and workers with BullMQ, (2) adding jobs (delayed, prioritized, repeatable, deduplicated), (3) setting up FlowProducer parent-child job hierarchies, (4) configuring retry strategies, rate limiting, or concurrency, (5) implementing job schedulers with cron/interval patterns, (6) preparing BullMQ for production (graceful shutdown, Redis config, monitoring), or (7) debugging stalled jobs or connection issues
BullMQ expert for Redis-backed job queues, background processing, and reliable async execution in Node.js/TypeScript applications. Use when: bullmq, bull queue, redis queue, background job, job queue.