Pinecone · Embeddings

Build retrieval-augmented generation systems: pick a vector database and embedding model, choose retrieval and reranking strategies, and start from a LangGraph pipeline.

wshobson/agents

Designs retrieval-augmented generation systems: document chunking, embeddings, vector store setup, hybrid search, reranking and retrieval evaluation, with checks at each step.

Jeffallan/claude-skills

Memory is the cornerstone of intelligent agents. An agent skill from omer-metin/skills-for-antigravity.

omer-metin/skills-for-antigravity

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…

elementalsouls/Claude-BugHunter

INVOKE THIS SKILL when building ANY retrieval-augmented generation (RAG) system.

langchain-ai/langchain-skills

Probes Retrieval-Augmented Generation pipelines for indirect prompt injection via poisoned retrieved documents and embedding-space manipulation, using NVIDIA garak, Promptfoo red-team plugins, and…

mukul975/Anthropic-Cybersecurity-Skills

Expert in vector databases, embedding strategies, and semantic search implementation.

aiskillstore/marketplace

RAG: embeddings, chunking, hybrid search (BM25+vector), reranking, CRAG, multi-hop.

softspark/ai-toolkit

Build and query vector stores with LangChain 1.0 without getting burned by flipped score semantics, embedding-dim mismatches, reranker quirks, and chunk-splitter bugs.

jeremylongshore/tons-of-skills-marketplace

A skill your agent uses when operating a vector store as a data layer — choosing or migrating between Pinecone, Qdrant, Weaviate and pgvector; designing a collection or index (distance metric…

ericrisco/rsc-harness