moai-domain-ml
Enterprise Machine Learning specialist with TensorFlow 2.20.0, PyTorch\ \ 2.9.0, \nScikit-learn 1.7.2 expertise. Master AutoML, neural architecture search,\ \ MLOps \nautomation, and production ML deployment. Build scalable ML pipelines\ \ with \ncomprehensive monitoring and experiment tracking.\n"
npx skills add majiayu000/claude-skill-registry --skill moai-domain-ml-jg-chalk-io-nora-livekit-2 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Provides an enterprise ML toolkit that combines deep learning and classical ML capabilities (TensorFlow 2.20.0, PyTorch 2.9.0, Scikit-learn 1.7.2) with AutoML, MLOps, and deployment support. Includes data processing, model training with experiment tracking, and evaluation utilities.
How it works
- Data Processing Pipeline: A MlDataProcessor class auto-detects numeric and categorical features, builds a preprocessing pipeline with imputation, scaling, and one-hot encoding, and can transform data while generating feature names.
- Model Training with Experiment Tracking: An ExperimentTracker class initializes MLflow tracking, logs parameters, trains the model, computes train/validation metrics (accuracy, precision, recall, F1, etc.), and logs metrics and artifacts. It supports logging feature importances and saving the model or model info.
- Model Evaluation and Validation: A ModelEvaluator class can evaluate classifications by computing metrics (accuracy, precision_macro, recall_macro, F1, precision_weighted, recall_weighted, F1_weighted), produce a classification report and confusion matrix, perform cross-validation, generate a learning curve, and assemble a detailed evaluation report.
When to use it
Use when building end-to-end ML projects requiring structured data processing, model training with experiment tracking, and thorough evaluation, within MLflow-enabled workflows.
What it can touch
- Tools: Read, Write, Edit, Bash, Glob, WebFetch, WebSearch (as listed in allowed-tools)
- References to sklearn, mlflow, TensorFlow, PyTorch, and supporting libraries are present within code examples and class implementations.
Caveats
- The skill is labeled stable and MIT licensed; updated 2025-11-18.
- It assumes availability of external libraries (MLflow, scikit-learn, TensorFlow, PyTorch) in the execution environment.
# Enterprise Machine Learning ## Level 1: Quick Reference ### Core Capabilities - **Deep Learning**: TensorFlow 2.20.0, PyTorch 2.9.0, JAX 0.4.33 - **Classical ML**: Scikit-learn 1.7.2, XGBoost 2.0.3, LightGBM 4.4.0 - **AutoML**: H2O AutoML 3.44.0, AutoGluon 1.0.0, TPOT 0.12.2 - **MLOps**: MLflow 2.9.0, Kubeflow 1.8.0, DVC 3.48.0 - **Deployment**: ONNX 1.16.0, TensorFlow Serving, TorchServe, Seldon Core ### Quick Setup Examples ```python # TensorFlow 2.20.0 with modern Keras API import tensorflow as tf from tensorflow import keras # Create a simple neural network model = keras.Sequential([ keras.layers.Dense(128, activation='relu', input_shape=(784,)), keras.layers.Dropout(0.2), keras.layers.Dense(64, activation='relu'), keras.layers.Dropout(0.2), keras.layers.Dense(10, activation='softmax') ]) model.compile( optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'] ) # Train with modern callbacks callbacks = [ keras.callbacks.EarlyStopping(patience=5, restore_best_weights=True), keras.callbacks.ReduceLROnPlateau(factor=0.5, patience=3), keras.callbacks.ModelCheckpoint('best_model.h5', save_best_only=True) ] # model.fit(X_train, y_train, validation_data=(X_val,
- Level 1: Quick Reference
- Core Capabilities
- Quick Setup Examples
- Level 2: Practical Implementation
- ML Pipeline Architecture
- AutoML Implementation
- Level 3: Advanced Integration
- MLOps and Production Deployment
- Related Skills
- Quick Start Checklist
- Performance Optimization Tips
What does the moai-domain-ml skill do?
Enterprise Machine Learning specialist with TensorFlow 2.20.0, PyTorch\ \ 2.9.0, \nScikit-learn 1.7.2 expertise. Master AutoML, neural architecture search,\ \ MLOps \nautomation, and production ML deployment. Build scalable ML pipelines\ \ with \ncomprehensive monitoring and experiment tracking.\n"
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill moai-domain-ml-jg-chalk-io-nora-livekit-2 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.