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Search arXiv for preprints and academic papers by topic with abstract retrieval. \n \n Query-based search across physics, mathematics, computer science, biology, statistics, and related fields \n Configurable result limit (default 10 papers) with results sorted by relevance \n Returns title and abstract for each matching paper \n Requires the arxiv Python package; install via pip if not already present \n

Expert patterns for Algolia search implementation, indexing strategies, and React InstantSearch integration. \n \n React InstantSearch with hooks (useSearchBox, useHits, useRefinementList, usePagination) for type-ahead search and faceted filtering in modern React applications \n Next.js SSR support via react-instantsearch-nextjs, compatible with both Pages Router and App Router, with URL synchronization and dynamic rendering configuration \n Three indexing strategies: full reindexing, full recor

Semantic code search by meaning rather than exact text strings. \n \n Searches code by intent and concept similarity using embeddings, returning ranked results with relevance scores (0.0–1.0) \n Requires GrepAI initialization, an active index created via grepai watch , and a running embedding provider like Ollama \n Supports natural language queries describing behavior or intent; 3–7 word phrases work best, with results limited via --limit flag \n Interprets scores: 0.90+ excellent match, 0.80–0

Search GitHub code, repositories, issues, and pull requests with flexible filtering. \n \n Four search types: code, repositories, issues, and pull requests, each with full GitHub search syntax support \n Optional filtering by repository owner and name to narrow results to specific projects \n Requires GitHub personal access token configured in mcp_config.json for authentication \n Command-line interface with straightforward parameters for integration into agent workflows \n

Rust-powered vector database for production RAG with hybrid search and distributed scaling. \n \n Supports dense, sparse, and multi-vector storage per point with four distance metrics (COSINE, EUCLID, DOT, MANHATTAN) and HNSW indexing for fast nearest-neighbor search \n Rich filtering during search across any payload field, with optional payload indexing for performance and support for complex boolean queries \n Quantization options (scalar, product, binary) and on-disk storage for memory effici

Natural language semantic search across arXiv's complete preprint archive in physics, math, and computer science. \n \n Accepts plain English queries without requiring complex search parameter syntax; powered by Valyu's semantic search API \n Returns full article text, abstracts, and embedded figures from matching papers with relevance scores \n Requires a free Valyu API key ($10 credits available) and Node.js 18+ \n Supports filtering by result count and integrates with jq for extracting titles

Intelligent multi-engine search with automatic network detection and fallback prioritization. \n \n Supports four search engines with two priority modes: quality-first (Tavily > DuckDuckGo > Bing API > Bing scraper) or balanced/free-first (DuckDuckGo > Tavily > Bing API > Bing scraper) \n Includes automatic quota management for API-based engines, 5-minute network detection caching, and forced re-detection after network changes \n Provides web content fetching and batch enrichment of search resul

Search Gmail messages by query and export results for offline review. \n \n Requires the gws-gmail skill as a prerequisite dependency \n Supports Gmail query syntax for filtering by sender, date, labels, and other message properties \n Exports search results to JSON format for further processing or archival \n Retrieves full message details including headers and body content for selected emails \n

Efficient similarity search patterns for vector databases and semantic retrieval systems. \n \n Covers four major vector database implementations: Pinecone, Qdrant, pgvector with PostgreSQL, and Weaviate, each with production-ready code templates \n Explains three index types (Flat, HNSW, IVF+PQ) with trade-offs between search speed, recall accuracy, and data scale \n Includes four distance metrics (Cosine, Euclidean, Dot Product, Manhattan) and guidance on when to use each \n Demonstrates hybri

Search your coding memory for past work, sessions, and implementation details. \n \n Queries personal session memories, project/repo memories, or both in parallel using optional scope flags ( --user , --repo , --both ) \n Returns formatted results with timestamps and relevance scores to help recall previous decisions, implementations, and coding patterns \n Designed for retrieving context about past work when users ask about earlier sessions, how something was implemented, or what they worked on