AI & LLM Engineering · Pinecone

Fetches current documentation for third-party APIs and SDKs with the chub CLI before the agent writes code against them, instead of relying on remembered API shapes.

andrewyng/context-hub

Guide to using and contributing cognee community packages: database adapters, data-source connectors, custom tasks and retrievers, and Keywords AI observability.

topoteretes/cognee

A skill your agent uses when you need documentation for a third-party library, SDK, or API before writing code that uses it — for example, "use the OpenAI API", "call the Stripe API", "use the…

sudomakes/backroad

Shows how to use Pinecone, a managed vector database, for production RAG, semantic search and recommendations: indexes, upserts, queries, filters and namespaces.

Orchestra-Research/AI-Research-SKILLs

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

Vector database implementation for AI/ML applications, semantic search, and RAG systems.

ancoleman/ai-design-components

Build production RAG pipelines and persistent agent memory using Pinecone as the vector database backend.

github/awesome-copilot

Agent RAG and long-term memory with Pinecone. An agent skill from Luciole-Studio/Misaka-Agent.

Luciole-Studio/Misaka-Agent

Security-first SOP for multi-tenant RAG systems. An agent skill from agentsope/SkillAlchemy.

agentsope/SkillAlchemy

Managed vector DB for production RAG and search. An agent skill from Luciole-Studio/Misaka-Agent.

Luciole-Studio/Misaka-Agent

Build GraphRAG retrieval pipelines on Neo4j using the neo4j-graphrag Python package (v1.22.0+).

neo4j-contrib/neo4j-skills

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

Managed vector database for production RAG — serverless and pod-based deployment, hybrid search, namespaces, and metadata filtering.

AlexAI-MCP/hermes-CCC

Every Pinecone API feature, plus local sync, snapshot history, and text-first search no other Pinecone tool has.

mvanhorn/printing-press-library