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Found 44 Skills
Run the Upstash CLI (`upstash`) against the Upstash Developer API for Redis, Vector, Search, QStash, and teams. Use when listing or managing databases, backups, vector/search indexes, QStash instances, team members, stats, or any non-interactive Upstash automation with JSON output and terminal commands.
OpenSearch development best practices for indexing, querying, search optimization, vector search, and cluster management
Review and improve HelixDB query performance and query shape. Use when the task is to optimize a slow Helix query, improve anchor choice, tighten index usage, reduce traversal breadth, slim projections, fix BM25 or vector search scope, or decide between stored and dynamic routes.
Expert in deploying and customizing a modular RAG system with MCP protocol for AI assistants
Discover available tools and resources in Databricks workspace. Use when: (1) User asks 'what tools are available', (2) Before writing agent code, (3) Looking for MCP servers, Genie spaces, UC functions, or vector search indexes, (4) User says 'discover', 'find resources', or 'what can I connect to'.
Clean code patterns for Azure AI Search Python SDK (azure-search-documents). Use when building search applications, creating/managing indexes, implementing agentic retrieval with knowledge bases, or working with vector/hybrid search. Covers SearchClient, SearchIndexClient, SearchIndexerClient, and KnowledgeBaseRetrievalClient.
Use these skills to set up and optimize production-ready vector workloads by simply expressing your intent and performance requirements.
Query integrated indexes using text with Pinecone MCP. IMPORTANT - This skill ONLY works with integrated indexes (indexes with built-in Pinecone embedding models like multilingual-e5-large). For standard indexes or advanced vector operations, use the CLI skill instead. Requires PINECONE_API_KEY environment variable and Pinecone MCP server to be configured.