Pinecone
DatabaseCommonly used with
Skills using Pinecone (12)
chroma
Embedding database for RAG and semantic search.
faiss
Fast vector similarity search at billion scale.
assessing-vector-and-embedding-weaknesses
Test vector stores for embedding inversion, cross-tenant leakage, and poisoning.
retrieval
Retrieval - vector DBs, embeddings, hybrid search, reranking.
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.
ai-engine-wordpress-mcp-server-and-ai-automation
AI Engine is a WordPress plugin by Meow Apps that connects sites to OpenAI, Claude, Gemini, and other models while exposing WordPress actions through MCP and REST interfaces. This skill helps agents configure providers, enable the plugin's MCP capabilities, and automate content, chatbots, media, and site-management workflows from WordPress.
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
ccc-data
complete data ecosystem — 8 skills in one. Data pipelines, SQL optimization, visualization, machine learning, data quality, analytics, reporting, and vector search.
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.
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.
rag-specialist
Build Retrieval Augmented Generation (RAG) pipelines with vector databases, embeddings, and context-aware responses. Adapted from Anthropic's Claude Cookbooks.
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