Langchain

A comprehensive guide for selecting and optimizing embedding models for vector search and RAG applications. Provides code templates for Voyage AI, OpenAI, and local embedding models, along with chunking strategies, domain-specific pipelines, and quality evaluation methods.

This skill provides comprehensive guidance for building sophisticated LLM applications using LangChain 1.x and LangGraph. It covers agent orchestration with StateGraph, RAG implementations, multi-agent systems, memory management patterns, and production deployment best practices including LangSmith observability integration.

This skill provides comprehensive documentation and code examples for building RAG (Retrieval-Augmented Generation) systems. It covers vector database setup (Pinecone, Weaviate, Chroma, pgvector), embedding strategies, retrieval patterns (hybrid search, HyDE, multi-query), chunking strategies, and evaluation metrics using LangChain and LangGraph.

A comprehensive prompt engineering skill that teaches advanced techniques like chain-of-thought, few-shot learning, structured outputs, and prompt optimization. Includes reference documentation, template libraries, and a Python script for A/B testing prompts to improve LLM performance.

A comprehensive plugin for building production-ready LLM applications, including RAG systems with vector databases, AI agents using LangGraph, advanced prompt engineering patterns, and embedding strategies. It provides templates and best practices for integrating with Pinecone, Qdrant, pgvector, and other vector stores.

Stripe's official AI integration toolkit that enables AI agents to interact with Stripe APIs for payments, subscriptions, and billing. Supports popular frameworks like LangChain, Vercel AI SDK, and CrewAI, and includes utilities for metering LLM token usage through Stripe's billing infrastructure.