Embeddings
Vector embeddings and similarity
Uses and selection guide
Use these skills to connect model capabilities with repeatable agent workflows. Check the model or tool requirements, context inputs and evaluation examples; choose the smallest workflow that covers your actual task.
gpu-document-processing
by langchain-ai
Use when processing large PDFs, document collections, or bulk text extraction tasks that benefit from GPU-accelerated processing. Triggers when the user provides large documents or needs bulk document analysis.
chroma
by NousResearch
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.
faiss
by NousResearch
Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.
agent-memory-systems
by sickn33
"Memory is the cornerstone of intelligent agents. Without it, every
agent-memory-mcp
by sickn33
"A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions)."
generate-rag-dataset
by langwatch
Generate a synthetic evaluation dataset from your RAG knowledge base. Creates diverse Q&A pairs with expected answers and relevant context, ready for LangWatch experiments and platform import. Use when you need test data for your RAG pipeline.
rag-architect
by Jeffallan
Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality. Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, context augmentation, similarity search, or embedding-based indexing.
rag-chatbot
by elastic
Guide for building RAG (retrieval-augmented generation) chatbots and Q&A systems with Elasticsearch. Use when a developer wants to build a chatbot, Q&A system, or AI assistant that answers questions from their own data.
elasticsearch-onboarding
by elastic
Primary guided playbook for Elasticsearch search in Kibana Agent Builder: intent → data → mapping → Dev Tools API snippets (SENSE), with one question at a time. Load this skill whenever the user wants to learn Elasticsearch search, get started, begin building, take first steps, onboard, follow a walkthrough or tutorial, go from zero to a working query, or get structured help setting up indices and search — including casual openers like hi, help, getting started, new to Elasticsearch, how do I build search, or I want to try search. Use when they need end-to-end onboarding, not a single narrow API answer. If they only ask what they can build with Elastic (exploration without the full playbook), prefer invoking /use-case-library first; you can still load this skill afterward for the guided build.
use-case-library
by elastic
Elasticsearch use case library — the full map of what you can build, with industry examples and technologies. Use when a developer asks "what can Elastic do?", "what can I build?", "what use cases does Elasticsearch support?", or needs help choosing what to build.
vector-hybrid-search
by elastic
Guide for building vector search, hybrid search, and using Elasticsearch as a vector database. Covers semantic_text, dense_vector, embedding strategies, hybrid BM25+kNN via RRF, reranking, and production optimization. Use when a developer wants semantic search, hybrid search, kNN, embeddings, or Elasticsearch as a vector store.
analyzing-typosquatting-domains-with-dnstwist
by mukul975
Detect typosquatting, homograph phishing, and brand impersonation domains using dnstwist to generate domain permutations and identify registered lookalike domains targeting your organization.
bioservices
by K-Dense-AI
Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use biopython.
chembl-database
by K-Dense-AI
Query ChEMBL bioactive molecules and drug discovery data. Search compounds by structure/properties, retrieve bioactivity data (IC50, Ki), find inhibitors, perform SAR studies, for medicinal chemistry.
bindingdb-database
by K-Dense-AI
Query BindingDB for measured drug-target binding affinities (Ki, Kd, IC50, EC50). Search by target (UniProt ID), compound (SMILES/name), or pathogen. Essential for drug discovery, lead optimization, polypharmacology analysis, and structure-activity relationship (SAR) studies.
Obsidian Vault
by mattpocock
```
abstract-invariant-generator
by ArabelaTso
Uses abstract interpretation to automatically infer loop invariants, function preconditions, and postconditions for formal verification. Generates invariants that capture program behavior and support correctness proofs in Dafny, Isabelle, Coq, and other verification systems. Use when adding formal specifications to code, generating verification conditions, inferring contracts for functions, or discovering loop invariants for proofs.
code-search-assistant
by ArabelaTso
Search code repositories for code related to a given code snippet, ranking results by call chain similarity, textual similarity, and functional similarity. Use when finding related code, locating similar implementations, discovering code dependencies, or identifying code that performs similar operations. Outputs ranked file lists with matching code snippets and relevance scores.
mcp:setup-arxiv-mcp
by NeoLabHQ
Guide for setup arXiv paper search MCP server using Docker MCP
pinecone
by NousResearch
Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
pinecone
by NousResearch
Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
faiss
by NousResearch
Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.
qdrant-vector-search
by NousResearch
High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.
chroma
by NousResearch
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.