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Found 61 Skills
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.
Search conversation history and semantic memory to recall previous discussions, decisions, and context. Use when the user asks to "search memory", "what did we discuss", "remember when", "find previous conversation", "check history", or before starting work to recall prior decisions.
Guide for using the `paper` CLI tool — a local academic paper management system with AI-powered vector search. Use this skill whenever the user wants to manage academic papers, create knowledge bases, add PDFs to a knowledge base, search papers semantically, configure embedding models, or manage literature metadata and notes. Also trigger when the user mentions "paper" CLI, knowledge bases for research, literature management, or wants to query their paper collection. Even if the user just says something like "add this PDF" or "search my papers" in a project that uses paper-manager, this skill should activate.
Kinetica SQL query knowledge. Activate when the user is writing analytical queries for Kinetica, asking about Kinetica-specific functions, or working with geospatial, time-series, graph, or vector data.
Vector search with SurrealDB using HNSW indexes, KNN queries, and similarity scoring. Use when creating vector indexes, querying vectors with KNN distance operators, building semantic search or RAG pipelines, tuning HNSW parameters (EFC, M, M0, distance function, type), or implementing recommendation systems with SurrealDB. Triggers: HNSW, vector, embedding, KNN, cosine, euclidean, semantic search, RAG, vector::distance.
Apache Cassandra distributed database for high availability. Use for distributed systems.
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'.
Build vector retrieval with DashVector using the Python SDK. Use when creating collections, upserting docs, and running similarity search with filters in Claude Code/Codex.
Alicloud OSS AI Content Awareness Skill. Use for enabling and querying OSS semantic search with AI-powered content understanding. Triggers: "OSS AI Content Awareness", "OSS semantic search", "OSS vector search", "search by text", "text-to-image search", "text-to-video search", "OSS MetaQuery", "OSS data index", "OSS AI内容感知", "OSS语义检索", "OSS向量检索", "以文搜图", "以文搜视频", "OSS数据索引"
Pinecone integration. Manage Indexs. Use when the user wants to interact with Pinecone data.
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.