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Found 14 Skills
Search OpenSearch documentation, blogs, and community forums. Use when the user asks about OpenSearch features, configuration, APIs, troubleshooting, k-NN, neural search, cluster settings, index mappings, query DSL, or any OpenSearch-related questions.
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 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.
Use OpenSearch vector search edition via the Python SDK (ha3engine) to push documents and run HA/SQL searches. Ideal for RAG and vector retrieval pipelines in Claude Code/Codex.
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 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.
Expert in migrating Apache Solr collections to OpenSearch indexes. Translates Solr XML/JSON schemas to OpenSearch mappings and converts Solr syntax (Standard, DisMax, eDisMax) into OpenSearch DSL. Provides sizing for nodes, shards, and JVM heap. Provides guidance auf authentication migration from Solr to OpenSearch. Uses the AWS Knowledge MCP Server for accurate, up-to-date OpenSearch and AWS service information.
OpenSearch development best practices for indexing, querying, search optimization, vector search, and cluster management
Smoke test for alicloud-ai-search-opensearch. Validate minimal authentication, API reachability, and one read-only query path.
Use when external agents must construct PubFi DSL requests for OpenSearch and Postgres without server-side natural language compilation.
Use this skill whenever planning, designing, reviewing, or improving search and recommendation systems for a two-sided trust marketplace built on OpenSearch — covers user-intent framing, product-surface architecture, index design, query understanding, retrieval strategy, ranking, search-plus-recs blending, measurement, and a dashboard-and-alerting layer for ongoing decision making. Triggers on tasks involving marketplace search, homefeeds, ranking, relevance tuning, OpenSearch query DSL, analyzers, synonyms, golden sets, NDCG, A/B testing, or diagnosing an existing retrieval system. Use this skill BEFORE marketplace-personalisation when planning new work; hand off when the diagnosed bottleneck is personalisation-specific.
DigitalOcean Managed Databases for PostgreSQL, MySQL, Redis, MongoDB, Kafka, OpenSearch, and Valkey. Use when provisioning, scaling, or operating managed database clusters on DigitalOcean.
Generates Tzatziki-based Cucumber BDD tests (.feature files) from a functional specification. Use this skill whenever a user wants to write Cucumber tests, add BDD scenarios, create feature files, generate tests, or test application behaviors with Gherkin — especially in Java/Spring projects using Tzatziki step definitions for HTTP, JPA, Kafka, MongoDB, OpenSearch, logging, or MCP. Also use when the user mentions writing integration tests, acceptance tests, or end-to-end tests in a project that already has Tzatziki/Cucumber dependencies, including TestNG-based setups.
Store and query vector embeddings using Amazon S3 Vectors, a cost-effective long-term vector storage service with its own API namespace (s3vectors). Triggers on: create S3 vector bucket, vector index, store embeddings, semantic search, RAG vector storage, similarity search, vector database, migrate from other vector databases. Do NOT use for: querying tabular data (use querying-data-lake), S3 object storage, or hundreds/thousands of sustained QPS (use OpenSearch).