OpenSearch

OpenSearch skills to help set up and deploy OpenSearch for a variety of use cases: build search applications with semantic, hybrid, neural sparse, and agentic search strategies; analyze observability data with log analytics (PPL and Query DSL) and distributed traces (OpenTelemetry); deploy to Amazon OpenSearch Service or OpenSearch Serverless with Bedrock integration for embeddings and RAG. Just ask your AI assistant to set up a search app, query logs, investigate traces, or deploy to AWS.

Created by OpenSearchView Source

Skills1

opensearch-skillsBuild search applications and query log analytics data with OpenSearch. Use this skill when the user mentions OpenSearch, search app, index setup, search architecture, semantic search, vector search, hybrid search, BM25, dense vector, sparse vector, agentic search, RAG, embeddings, KNN, PDF ingestion, document processing, or any related search topic. Also use for log analytics and observability — when the user wants to set up log ingestion, query logs with PPL, analyze error patterns, set up index lifecycle policies, investigate traces, or check stack health. Activate even if the user says log analysis, Fluent Bit, Fluentd, Logstash, syslog, traceId, OpenTelemetry, or log analytics without mentioning OpenSearch.

MCP4

opensearch-mcp-server

ddg-search

awslabs.aws-api-mcp-server

aws-knowledge-mcp-server