mlx

ML Workbench for Claude Code. Full ML lifecycle: search papers across 7 academic sources, discover and download datasets from 5 repositories, explore and clean data, engineer features, train models (Naive Bayes, KNN, LDA/QDA, SVM, Decision Trees, Ensembles, GLM, Gaussian Process, Neural Networks), run autonomous experiments, build AI apps with LLMs and RAG, build MCP servers, deploy models with Docker and CI/CD, detect drift, explain predictions with SHAP, generate podcasts from papers, manage notebooks, extract YouTube content, and learn ML interactively with 3 university-grade courses (CS229, Applied ML, ML Engineering). 11 agents, 16 skills, 3 CLI tools (mlx-exp, mlx-search, mlx-status), 1 MCP server, 3 output styles, Python LSP via pyright.