Machine Learning

datarobot-automation helps automate DataRobot operations through Composio Rube MCP, with tool discovery and connection checks before execution.

bigml-automation helps agents automate BigML tasks through Composio Rube MCP by searching current tools first, checking the BigML connection, and using returned schemas before execution.

senior-ml-engineer helps agents plan production ML systems: model deployment, MLOps pipelines, monitoring, drift detection, RAG architecture, and LLM integration. Includes reference guides and starter scripts for deployment, monitoring, and RAG that teams should adapt before production.

senior-data-scientist is a Claude skill for A/B test design, causal reasoning, feature engineering, and tabular ML evaluation. Use it to guide sample sizing, metric choice, leakage checks, SHAP review, and MLflow-style tracking; scripts are scaffold templates, not complete engines.

torchdrug is a PyTorch-native toolkit for molecular and protein machine learning. Use the torchdrug skill to choose tasks, datasets, and modular models for graph neural networks, protein modeling, knowledge graph reasoning, molecular generation, and retrosynthesis. It is best for custom model development and reproducible configs, not just canned demos.

torch-geometric skill guide for PyTorch Geometric graph neural networks. Use it for torch-geometric install help, torch-geometric usage, graph classification, node classification, link prediction, heterogeneous graphs, custom MessagePassing layers, and scaling GNNs for Machine Learning workflows.

The transformers skill helps you use Hugging Face Transformers for model loading, inference, tokenization, and fine-tuning. It is a practical transformers guide for Machine Learning tasks across text, vision, audio, and multimodal workflows, with clear paths for quick baselines and custom training.

stable-baselines3 skill guide for Machine Learning workflows: train RL agents, wire Gymnasium environments, and choose PPO, SAC, DQN, TD3, DDPG, or A2C with less guesswork. Best for standard single-agent reinforcement learning, quick prototyping, and practical stable-baselines3 usage.

shap skill for model interpretability and explainable AI. Use it to understand predictions, compute feature attributions, choose SHAP plots, and debug model behavior for Data Analysis across tree, linear, deep learning, and black-box models.

scvi-tools is a Python framework for probabilistic single-cell analysis. Use this scvi-tools skill for batch correction, latent embeddings, differential expression with uncertainty, transfer learning, and multimodal integration. It is a strong fit for single-cell RNA-seq, ATAC, CITE-seq, multiome, and spatial workflows, especially for advanced Machine Learning use cases.

scvelo is a Python skill for RNA velocity analysis in single-cell RNA-seq data. Use it to estimate cell state transitions from unspliced and spliced mRNA, infer trajectory direction, compute latent time, and identify driver genes. It is especially useful for scvelo for Data Analysis when you need directionality beyond standard clustering or pseudotime.

scikit-survival skill for survival analysis and time-to-event modeling in Python. Use this guide for censored data, Cox models, random survival forests, gradient boosting, Survival SVMs, and survival metrics like concordance index and Brier score.

scikit-learn helps you build classical machine learning workflows in Python. Use this scikit-learn skill for classification, regression, clustering, preprocessing, model evaluation, hyperparameter tuning, and pipelines. It’s a practical scikit-learn guide for tabular data and repeatable model development.