opensearch-skills
OpenSearch Skills provides a modular toolkit for constructing search applications with advanced retrieval strategies including vector and semantic search, plus observability features for log analysis and trace investigation. Deploy locally or to AWS, process documents at scale, and route queries intelligently across search, observability, and cloud infrastructure tasks.
OpenSearch Skills helps you build search applications with vector and semantic search, plus log analytics and AWS deployment capabilities.
AI-generated summary based on this skill's SKILL.md
Decision gist · record as of 2026-07-22
OpenSearch Skills helps you build search applications with vector and semantic search, plus log analytics and AWS deployment capabilities. OpenSearch Skills provides a modular toolkit for constructing search applications with advanced retrieval strategies including vector and semantic search, plus observability features for log analysis and trace investigation. Deploy locally or to AWS, process documents at scale, and route queries intelligently across search, observability, and cloud infrastructure tasks.
Use it when
- OpenSearch Skills supports both vector and semantic search through k-NN similarity search and dense vector embeddings.
- OpenSearch Skills enables log analytics through PPL (Piped Processing Language) queries and trace analytics for debugging.
Verify before relying
Read SKILL.md below before installing (60 files). Open directory: indexed for reading, not audited.
Install
opensearch-project/opensearch-agent-skills/opensearch-skills · repository language: Python
Open directory. Skills are indexed for reading, not audited. Review a skill's body before installing it.
Frequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
How do I build a search application with OpenSearch?
OpenSearch Skills helps you construct search applications by providing modular components for vector search, semantic search, and hybrid retrieval. Start by setting up an OpenSearch cluster (locally via Docker or on AWS), define your index schema with appropriate field mappings, ingest your documents, and configure search queries using BM25, dense vectors, or hybrid approaches that combine multiple ranking strategies for optimal relevance.
What are OpenSearch vector search and semantic search setup steps?
OpenSearch Skills supports both vector and semantic search through k-NN similarity search and dense vector embeddings. To set up: create an index with a vector field using the appropriate dimension and similarity metric (cosine, L2, or inner product), generate embeddings for your documents using an embedding model, ingest the documents with their vector representations, and query using the k-NN search API or combine vectors with BM25 for hybrid search.
How can I ingest and query logs for observability with OpenSearch?
OpenSearch Skills enables log analytics through PPL (Piped Processing Language) queries and trace analytics for debugging. Ingest logs using Fluent Bit or Logstash, structure them as JSON documents in OpenSearch indices, then query error patterns, trace events, and system health using PPL syntax or standard queries. The observability stack helps you correlate logs and traces to investigate issues across your infrastructure.
How do I set up OpenSearch for RAG with document embeddings?
OpenSearch Skills supports RAG (Retrieval-Augmented Generation) workflows by processing documents at scale and indexing them with embeddings. Convert documents (PDFs, text files) to JSONL format, generate embeddings for each document chunk, create an index with vector fields, ingest the documents, and query using semantic search to retrieve relevant context for your LLM applications.
What deployment options does OpenSearch Skills support?
OpenSearch Skills covers both local and cloud deployments. Run OpenSearch locally using Docker for development and testing, or deploy to AWS using managed services or EC2 instances. Configure index lifecycle policies for data retention, set up ingestion pipelines via Fluent Bit or Logstash, and manage cluster health and observability across your chosen infrastructure.
How do I implement hybrid search combining BM25 and dense vectors?
OpenSearch Skills enables hybrid search by combining BM25 keyword matching with dense vector similarity. Create an index with both text fields and vector fields, ingest documents with both representations, then use a query that scores both BM25 and k-NN results and combines them (via normalization and weighting) to balance lexical and semantic relevance for improved search quality.
SKILL.md
Rendered from the published skill. Quoted content, verbatim.
OpenSearch Skills
This is the top-level skill for OpenSearch. It contains four category skills that can also be installed and used independently:
| Category | Skill | Install individually |
|---|---|---|
| search |
(truncated - see the full file via the links below)
File tree — 15 files
skills/opensearch-skills/SKILL.md
skills/opensearch-skills/cli-reference.md
skills/opensearch-skills/cloud/SKILL.md
skills/opensearch-skills/cloud/aws-setup/SKILL.md
skills/opensearch-skills/cloud/aws-setup/aos/domain-01-provision.md
skills/opensearch-skills/cloud/aws-setup/aos/domain-02-deploy-search.md
skills/opensearch-skills/cloud/aws-setup/aos/domain-03-agentic-setup.md
skills/opensearch-skills/cloud/aws-setup/aoss/aoss-nextgen-provisioning/ADVANCED.md
skills/opensearch-skills/cloud/aws-setup/aoss/aoss-nextgen-provisioning/DEPROVISION.md
skills/opensearch-skills/cloud/aws-setup/aoss/aoss-nextgen-provisioning/ERRORS.md
skills/opensearch-skills/cloud/aws-setup/aoss/aoss-nextgen-provisioning/SKILL.md
skills/opensearch-skills/cloud/aws-setup/aoss/serverless-02-deploy-search.md
skills/opensearch-skills/cloud/aws-setup/aoss/serverless-04-agentic-setup.md
skills/opensearch-skills/cloud/aws-setup/reference.md
skills/opensearch-skills/cloud/managed-ingestion-service/SKILL.md
Let your AI agent find skills like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 56,283 agent skills by what they can do, searchable in plain language.
wish › “Build and deploy a search application with vector/semantic/hybrid search capabilities”
Give your agent the search over MCP, or paste the wish link into any chat. No install? Search from any chat →
Related skills
Provision and configure Amazon OpenSearch Service domains or Serverless collections, then deploy your search configurations to AWS. Handles infrastructure setup, IAM role configuration, Bedrock connector integration, and supports both managed domains and serverless deployments with optional agentic search capabilities.
Observability is a category skill for log analytics and trace investigation in OpenSearch. It routes you to specialized skills for querying logs to spot errors and patterns, or for investigating distributed traces to debug latency and service dependencies.
This skill guides you through building complete search applications on OpenSearch, covering index setup, document processing, and multiple search strategies including BM25, semantic, hybrid, and agentic approaches. Deploy ML models, ingest PDFs and other documents, and evaluate search quality end-to-end.
OpenSearch Launchpad is your guided path from initial requirements to a fully operational search system. It handles data ingestion, strategy selection (keyword, semantic, hybrid, or agentic), and deployment to either local Docker or Amazon OpenSearch Serverless. The skill walks you through architecture planning and launches a search UI ready for queries.
Cloud is a category skill for deploying OpenSearch search applications to AWS infrastructure. It guides you through provisioning Amazon OpenSearch Service domains or Serverless collections, configuring IAM roles, setting up Bedrock connectors for ML models, and migrating local setups to the cloud.
ingest is a category skill for transforming unstructured files into JSONL chunks on your machine. It handles PDF, DOCX, and PPTX formats through the document-processing skill, which uses Docling to produce search-ready output without requiring cloud infrastructure or AWS.
More skills trace-analytics (Apache-2.0) · document-processing (Apache-2.0) · managed-ingestion-service (Apache-2.0) · openhands-automation (MIT)