Weaviate
DatabaseCommonly used with
Skills using Weaviate (21)
hunt-rag-vector
Hunt vector-store / embedding-layer weaknesses in RAG pipelines (OWASP LLM08 Vector and Embedding Weaknesses) — persistent corpus poisoning that survives across sessions and users (distinct from one-shot indirect prompt injection, which is owned by hunt-llm-ai), cross-tenant vector-database IDOR (unauthenticated or unscoped queries against Pinecone/Weaviate/Chroma/Milvus/Qdrant/pgvector), source-text/metadata leakage in similarity-search results, and retrieval-hijack via adversarial embedding proximity ('SEO poisoning' for RAG). Targets: any app with a shared knowledge base, document upload feeding a chatbot, or a directly reachable vector-DB port. Validate: a second, clean session/account must inherit a poisoned result, or a cross-tenant artifact must be independently verifiable — confabulation is not a finding, same bar as hunt-llm-ai. Use when target is RAG-backed, exposes a vector-DB port, or lets users upload documents that other users' queries later retrieve.
rag
Implements document chunking, embedding generation, vector storage, and retrieval pipelines for Retrieval-Augmented Generation systems. Use when building RAG applications, creating document Q&A systems, or integrating AI with knowledge bases.
skill-builder
Automatically detect source types and build AI skills using Skill Seekers. Use when the user wants to create skills from documentation, repos, PDFs, videos, or other knowledge sources.
chroma
Embedding database for RAG and semantic search.
faiss
Fast vector similarity search at billion scale.
rag-architect
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.
retrieval
Retrieval - vector DBs, embeddings, hybrid search, reranking.
assessing-vector-and-embedding-weaknesses
Test vector stores for embedding inversion, cross-tenant leakage, and poisoning.
context-retrieval
Retrieve relevant information from a knowledge base using semantic, keyword, or hybrid search to ground a query. Use when the task starts with a corpus or index that must be searched; use context-ranking when candidate chunks already exist and only need ordering.
hybrid-search-architect
Designs a hybrid retrieval pipeline combining dense vector search and BM25 sparse search with reciprocal rank fusion, and explains when to use each configuration.
vector-databases
Vector database integration for embeddings and similarity search. Pinecone, Weaviate, Qdrant, ChromaDB, pgvector. Index management, metadata filtering, hybrid search, and production optimization. USE WHEN: user mentions "vector database", "embeddings", "similarity search", "Pinecone", "Weaviate", "Qdrant", "ChromaDB", "pgvector", "HNSW", "ANN" DO NOT USE FOR: LangChain integration - use `langchain`; RAG architecture - use `rag-patterns`; traditional databases - use database skills
chroma
Embedding database for RAG and semantic search.
faiss
Fast vector similarity search at billion scale.
chroma
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
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.
ai-native-development
Build AI-first applications with RAG pipelines, embeddings, vector databases, agentic workflows (ReAct, multi-agent, Opus 4.5), LLM integration, prompt engineering, streaming, and cost optimization. Use when: building an AI feature, integrating an LLM, setting up vector search, or designing agent architectures. Triggers on: AI app, LLM integration, RAG, vector database, agentic, prompt engineering, AI-native, semantic search, multi-agent, embeddings pipeline, cost optimization
chroma
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
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.
rag-poisoning
Expert methodology for attacking Retrieval-Augmented Generation (RAG) pipelines through document poisoning, index corruption, adversarial queries, and retrieval manipulation. For authorized red team assessments of AI search and Q&A systems.
pinecone
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.
senior-rag-engineer
Use when designing, building, reviewing, or operating retrieval augmented generation systems: corpus parsing, chunking, embedding, indexing, retrieval (semantic, lexical, hybrid), reranking, citation, evaluation, and ingestion freshness. Covers vector stores (pgvector, Pinecone, Weaviate, Qdrant, Vespa, Milvus, Elastic kNN), embedding models (text-embedding-3, bge-large, nomic-embed, voyage, cohere), BM25 and reciprocal rank fusion, cross encoder rerankers, ColBERT, MMR, and retrieval specific evaluation. Triggers: RAG, retrieval augmented generation, retrieval, embedding, vector store, hybrid search, BM25, reranker, citation, chunking, document parsing, freshness, retrieval eval, recall, precision, NDCG, MRR. Produces parsing plans, chunking configs, vector store schemas, hybrid retrieval pipelines, retrieval eval harnesses. Not for the LLM app around retrieval, see senior-llm-app-engineer; not for eval harness rigor, see senior-eval-engineer.
Integration detected automatically from skill content. Some results may be false positives.