qdrant-vector-search

This skill provides high-performance tokenization using HuggingFace's Rust-based library, processing 1GB of text in under 20 seconds. It supports BPE, WordPiece, and Unigram algorithms while enabling custom tokenizer training and alignment tracking. Use it when you need production-fast tokenization or to build custom tokenizers integrated with the transformers ecosystem.

CrewAI is a lightweight multi-agent orchestration framework for building teams of specialized AI agents that collaborate autonomously on complex tasks. It enables role-based agent collaboration with memory and supports sequential or hierarchical workflows for production use. The framework is built without LangChain dependencies for lean, fast execution.

Chroma is an open-source embedding database for AI applications that provides vector search, metadata filtering, and a simple API. It's ideal for building RAG applications and semantic search, scaling from local development to production. Use it when you need a self-hosted vector database for document retrieval and embedding storage.

This skill trains massive LLMs (2B-462B parameters) using NVIDIA's Megatron-Core framework for maximum GPU efficiency. Use it when training models over 1B parameters and needing advanced parallelism like tensor, pipeline, or expert parallelism. It's a production-ready framework proven on models like Nemotron and LLaMA.