“vector search and databases”

Store and query vector embeddings using Amazon S3 Vectors, a cost-effective long-term vector storage service with its own API namespace (s3vectors).

Databricks Vector Search endpoints and indexes for RAG and semantic search; covers index types, search modes, end-to-end RAG patterns

Vector search best practices for Azure DocumentDB using `cosmosSearch` — choosing between DiskANN / HNSW / IVF, creating indexes, tuning `lBuild` / `lSearch`…

简化版数据库工具，适用于火山引擎 RDS 实例以及自建数据库的元数据查询、SQL 执行、nl2sql 等场景。当用户需要查询火山引擎 RDS 实例以及自建数据库表结构、查看数据、执行 SQL 或将自然语言转换为 SQL 时使用此 skill。

Discovers requirements and generates architectural, design, and deployment guidance for dynamic hybrid search systems by combining semantic search and keyword…

行业数据查询与分析工具。接入多种合规公开数据源，支持精确匹配、模糊搜索、聚合统计、分组排序等查询方式。当前已覆盖工商企业基本信息、产业链节点区域指标、产业链企业信息、A股相关信息以及其他等大部分场景。完整的可用数据源清单及其字段结构需通过 describe_datasource 接口动态获取。…

Discovers requirements, and generates architectural, design, and deployment guidance for a retrieval-augmented generation (RAG)-capable enterprise search…

Use when the user wants to provision infrastructure or third-party services using Stripe Projects.

Guides and best practices for working with Lakebase Postgres on Neon: connections, pooled vs direct, schema migrations, branching, autoscaling, scale-to-zero…

Manages Amazon DocumentDB end-to-end — serverless-on-8.0 cluster setup, TLS/VPC/driver config, flexible-schema and vector-search data modeling, MongoDB…

Activate when developers have latent caching needs: slow API responses, database read bottlenecks, DynamoDB throttling or cost, RDS/Aurora scaling pressure…