Rust vector database with native NAPI bindings for Node.js, SIMD-accelerated HNSW search, and zero-copy operations. Use when the user needs maximum vector search performance in Node.js, SIMD-optimized distance calculations, native Rust bindings, or low-latency similarity search in server-side applications.
PostgreSQL AI vector database CLI with pgvector-compatible extension, 53+ SQL functions, HNSW/GNN/attention ops. Use when the user needs to manage PostgreSQL vector operations, install the RuVector extension, run vector/sparse/hyperbolic/graph/attention/GNN queries, benchmark PostgreSQL vector performance, or manage a PostgreSQL-backed AI database.
Standalone vector database with SQL, SPARQL, and Cypher query support powered by RuVector WASM. Use when the user needs a lightweight embedded vector database, multi-language query support (SQL/SPARQL/Cypher), standalone vector search without external dependencies, or a portable vector store for applications.
HTTP/gRPC server for RuVector with REST API, streaming, and authentication. Use when the user needs to deploy a vector database server, expose vector search over HTTP or gRPC, configure server authentication, manage collections via REST endpoints, or set up a production vector search service.
High-performance vector database for Node.js with native Rust NAPI and automatic WASM fallback. Use when the user needs to run vector similarity search, manage HNSW indexes, insert embeddings, or use the ruvector CLI for database operations, benchmarking, or ecosystem package management.
Analyzes Redis cache performance including hit rates, key distribution, memory usage, TTL patterns, and tenant isolation compliance
Generate Redis cache service with tenant-prefixed keys to prevent cross-tenant leakage, TTL management, and cache invalidation patterns. Use when caching data or reducing database load.
Best practices dùng Redis: caching patterns, session management, pub/sub, rate limiting và data structures.
Expert knowledge for Azure Cache for Redis development including troubleshooting, best practices, decision making, architecture & design patterns, security, configuration, integrations & coding patterns, and deployment. Use when configuring geo-replication, persistence, VNet/Private Link, CLI/PowerShell automation, or Blob import/export, and other Azure Cache for Redis related development tasks. Not for Azure Managed Redis (use azure-managed-redis), Azure HPC Cache (use azure-hpc-cache), Azure Blob Storage (use azure-blob-storage), Azure Table Storage (use azure-table-storage).
Redis performance optimization and best practices. Use this skill when working with Redis data structures, Redis Query Engine (RQE), vector search with RedisVL, semantic caching with LangCache, or optimizing Redis performance.
Specialized guide for NoSQL data modeling (Adapted for MongoDB).
Guide for using genies_cache dual-backend caching service. Use when implementing Redis or in-memory caching, managing TTL and atomic operations, or integrating caching into Genies microservices.
Redis performance optimization and best practices. Use this skill when working with Redis data structures, Redis Query Engine (RQE), vector search with RedisVL, semantic caching with LangCache, or optimizing Redis performance.
Episodic Memory Archiver. Stores full conversation transcripts with embeddings and analysis into ArangoDB. Tracks UNRESOLVED sessions for reflection with structured failure episodes (trigger/diagnosis/action/outcome), K~4 similar failure retrieval, user behavioral profiling, and federated taxonomy classification.
Build globally distributed applications with Azure Cosmos DB. Configure multi-region writes, consistency levels, partitioning, and change feed. Use for NoSQL databases, real-time analytics, and globally distributed data on Azure.
Comprehensive database architecture agent that provides expert guidance on database design, schema optimization, query performance tuning, and migration strategies. Covers relational and NoSQL databases, indexing strategies, normalization, data modeling, and scaling patterns. Use for database design, performance optimization, migration planning, or data architecture decisions.
Best practices dùng Elasticsearch 8+: index design, mappings, search queries, aggregations, relevance tuning và Node.js client patterns.
ALWAYS USE when building semantic layer, working with metrics/dimensions, or integrating dbt with Cube consumption APIs. Use IMMEDIATELY when working with Cube REST API, GraphQL queries, or Postgres wire protocol (psycopg2 connections). Provides research steps for data modeling, dbt integration, MEASURE() syntax, and API validation.
