opensearch-agent-skills

Plugin opensearch-agent-skills in opensearch-project/opensearch-agent-skills. Commands read from the repo's manifest — 1 month ago.

Skills (13)

• opensearch-skills

  Build 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 ingestio

• cloud

  Deploy OpenSearch search applications to managed cloud providers. Use this skill when the user wants to provision an OpenSearch domain or serverless collection on AWS, provision a managed OpenSearch service on Aiven, deploy search configurations, set up Bedrock connectors, configure IAM roles for Op

• aiven-setup

  Deploy OpenSearch search applications to Aiven for OpenSearch (a fully managed OpenSearch service across AWS, GCP, Azure, DigitalOcean, and UpCloud). Use this skill when the user wants to provision an Aiven OpenSearch service, deploy a search configuration to Aiven, migrate a local search setup to A

• aws-setup

  Deploy OpenSearch search applications to Amazon OpenSearch Service or Amazon OpenSearch Serverless. Use this skill when the user wants to provision an OpenSearch domain or serverless collection on AWS, deploy search configurations to AWS, set up Bedrock connectors, configure IAM roles for OpenSearch

• aoss-nextgen-provisioning

  Use when provisioning or deprovisioning OpenSearch Serverless collections, creating collection groups, setting up AOSS NextGen, or tearing down AOSS resources

• managed-ingestion-service

  Ingest documents at scale into Amazon OpenSearch using OpenSearch Ingestion Service (OSIS) pipelines. Upload pre-generated JSONL chunks to S3 and OSIS indexes them — optionally using semantic_enrichment in the sink to create the index with ASE automatically. Cloud ingestion — uploading raw PDF/DOCX

• ingest

  Process unstructured documents into search-ready chunks. Use this skill when the user wants to process PDFs or unstructured documents into JSONL chunks using Docling. Activate even if the user says document processing, chunking, Docling, PDF processing, or chunk quality evaluation. For cloud-scale i

• document-processing

  Process unstructured documents (PDF, DOCX, PPTX, XLSX) into search-ready JSONL chunks using Docling. Runs locally — no AWS or cloud services needed. Use this skill when the user wants to prepare documents for indexing, chunk documents, evaluate chunk quality, or convert PDFs to searchable text. Acti

• observability

  Analyze logs and investigate traces in OpenSearch. Use this skill when the user wants to query logs with PPL, analyze error patterns, discover log patterns, investigate traces, check stack health, or perform any observability task. Activate even if the user says log analysis, Fluent Bit, Fluentd, Lo

• log-analytics

  Analyze logs in OpenSearch using PPL and Query DSL. Use this skill when the user wants to query logs, analyze error patterns, discover log patterns, check error rates, perform anomaly detection on logs, or investigate application issues through log data. Activate even if the user says log analysis,

• trace-analytics

  Investigate distributed traces and spans in OpenSearch. Use this skill when the user wants to analyze traces, investigate slow spans, find error spans, track agent invocations, measure token usage, reconstruct trace trees, query service maps, or debug distributed systems through trace data. Activate

• search

  Build search applications with OpenSearch. Use this skill when the user mentions 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, search quality ev

• opensearch-launchpad

  Build search applications with OpenSearch from scratch. Use this skill when the user mentions 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 a

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