# @claudeautopm/plugin-ml Comprehensive Machine Learning plugin with **10 specialist agents** covering deep learning, classical ML, computer vision, NLP, time series, AutoML, and more. All patterns verified against official documentation via Context7 MCP server. **Version**: 2.0.0 | **Schema**: 2.0 | **Agents**: 10 | **Context7-Verified**: โœ… ## ๐Ÿ“ฆ Installation ```bash # Install the plugin package npm install @claudeautopm/plugin-ml # Or install globally npm install -g @claudeautopm/plugin-ml ``` ## Context7 Integration This plugin uses Context7 MCP server to ensure all ML patterns follow the latest official documentation: **Libraries Verified**: - **TensorFlow**: `/tensorflow/tensorflow` (5,192 snippets, trust 7.9) - **PyTorch**: `/pytorch/pytorch` (4,451 snippets, trust 8.4) - **Gymnasium**: `/farama-foundation/gymnasium` (288 snippets, trust 8.1) - **Stable-Baselines3**: `/dlr-rm/stable-baselines3` (265 snippets, trust 8.0) - **Scikit-learn**: `/scikit-learn/scikit-learn` (4,161 snippets, trust 8.5) All ML patterns, best practices, and code examples are verified against current official documentation to prevent outdated approaches and ensure production-ready implementations. ## ๐Ÿง  What's Included ### 10 Specialist Agents #### 1. tensorflow-keras-expert **TensorFlow and Keras deep learning specialist** - Model building: Sequential, Functional, Subclassing APIs - Training optimization: Callbacks, learning rate schedules, XLA compilation - Distributed training: MirroredStrategy, TPUStrategy - Mixed precision: Float16 for 2-3x speedup - Deployment: SavedModel, TensorFlow Lite for mobile/edge - Context7 patterns: 10+ verified TensorFlow best practices **Example**: ```markdown @tensorflow-keras-expert Build a CNN for MNIST digit classification with: - Data augmentation - Early stopping and checkpointing - XLA compilation for speedup - TensorFlow Lite export for mobile deployment ``` #### 2. pytorch-expert **PyTorch deep learning specialist** - Model building: torch.nn.Module, custom architectures - Training: torch.compile for 2x speedup, custom training loops - Distributed: DistributedDataParallel (DDP) for multi-GPU - Mixed precision: torch.amp for faster training - Optimization: Learning rate schedulers, gradient clipping - Context7 patterns: 10+ verified PyTorch best practices **Example**: ```markdown @pytorch-expert Create a ResNet-50 model for ImageNet with: - torch.compile optimization - DDP training on 4 GPUs - Mixed precision (FP16) - Cosine annealing LR schedule - Model checkpointing ``` #### 3. reinforcement-learning-expert **Reinforcement Learning specialist** - Environments: Gymnasium API, custom environment creation - Algorithms: PPO, SAC, TD3, DQN (via Stable-Baselines3) - Training: Q-Learning, policy gradients, actor-critic methods - Optimization: Reward shaping, vectorized environments - Callbacks: Early stopping, model checkpointing - Context7 patterns: 10+ verified RL best practices **Example**: ```markdown @reinforcement-learning-expert Train a PPO agent for CartPole-v1 with: - Stable-Baselines3 integration - Evaluation callbacks - Reward threshold stopping - Best model saving - Test agent visualization ``` #### 4. scikit-learn-expert **Classical machine learning specialist** - Pipelines: Preprocessing and modeling without data leakage - Model selection: GridSearchCV, RandomizedSearchCV - Cross-validation: KFold, StratifiedKFold - Feature engineering: Selection, scaling, encoding - Algorithms: Classification, regression, clustering - Context7 patterns: 5+ verified scikit-learn best practices **Example**: ```markdown @scikit-learn-expert Build a classification pipeline for customer churn with: - Pipeline with StandardScaler and RandomForest - GridSearchCV for hyperparameter tuning - 5-fold cross-validation - Feature importance analysis - Handle class imbalance with class_weight ``` #### 5. neural-network-architect **Neural network architecture design specialist** - Architecture patterns: CNNs, ResNets, Transformers, U-Net - Layer selection: Convolutions, normalization, activation functions - Skip connections: Residual blocks, encoder-decoder - Model scaling: Width, depth, resolution strategies - Context7 patterns: Architecture best practices **Example**: ```markdown @neural-network-architect Design a ResNet architecture for ImageNet with: - ResNet-50 structure with bottleneck blocks - Batch normalization and ReLU activations - Skip connections for gradient flow - Proper initialization strategies ``` #### 6. gradient-boosting-expert **Gradient boosting specialist (XGBoost, LightGBM, CatBoost)** - Model training: Early stopping, cross-validation - Hyperparameter tuning: GridSearchCV, Optuna - Feature importance: Gain, cover, SHAP values - Categorical handling: Native categorical support - Context7 patterns: 10+ verified XGBoost patterns **Example**: ```markdown @gradient-boosting-expert Build a XGBoost model for customer churn with: - Categorical feature handling (no encoding needed) - GridSearchCV hyperparameter tuning - Early stopping on validation set - SHAP feature importance analysis ``` #### 7. computer-vision-expert **Computer vision specialist (OpenCV, YOLO, object detection)** - Image preprocessing: Resizing, normalization, augmentation - Object detection: YOLO, Faster R-CNN with OpenCV DNN - Segmentation: DeepLab, Mask R-CNN - Face recognition: YuNet detection, SFace recognition - Real-time video: Optimized processing pipelines - Context7 patterns: OpenCV best practices **Example**: ```markdown @computer-vision-expert Build a real-time object detection pipeline with: - YOLO v8 with OpenCV DNN module - GPU acceleration (CUDA backend) - Video processing at 30 FPS - Non-maximum suppression for duplicate removal ``` #### 8. nlp-transformer-expert **NLP transformer specialist (BERT, GPT, T5)** - Fine-tuning: Text classification, NER, question answering - Pipeline API: Quick inference with pre-trained models - Text generation: GPT-2/GPT-3 with sampling strategies - Inference optimization: Mixed precision, torch.compile - Parameter-efficient fine-tuning: LoRA with PEFT - Context7 patterns: 15+ verified Transformers patterns **Example**: ```markdown @nlp-transformer-expert Fine-tune BERT for sentiment analysis with: - HuggingFace Trainer API - Mixed precision (FP16) training - Early stopping and checkpointing - LoRA for efficient fine-tuning (0.1% trainable params) ``` #### 9. time-series-expert **Time series forecasting specialist** - Statistical models: Prophet, ARIMA, SARIMAX - Deep learning: LSTM, GRU, Temporal Fusion Transformer - Seasonality: Trend analysis, decomposition - Anomaly detection: Prophet confidence intervals - Multi-step forecasting: Recursive and direct strategies - Context7 patterns: Prophet and ARIMA best practices **Example**: ```markdown @time-series-expert Build a sales forecasting model with: - Prophet for multiple seasonality (daily, weekly, yearly) - Custom holiday effects (Black Friday, Christmas) - Cross-validation for horizon evaluation - Anomaly detection with 99% confidence intervals ``` #### 10. automl-expert **Automated machine learning specialist** - Frameworks: AutoGluon, FLAML, AutoKeras, H2O AutoML - Model selection: Automatic algorithm comparison - Ensembling: Stacking, blending, bagging - Hyperparameter optimization: Bayesian search - Rapid prototyping: Get best models with minimal code - Context7 patterns: AutoGluon and FLAML workflows **Example**: ```markdown @automl-expert Build a classification model with AutoGluon: - Automatic preprocessing and feature engineering - Train 10+ model types (LightGBM, XGBoost, Neural Nets) - Multi-layer stacking ensembles - 1 hour time budget with best_quality preset ``` ## ๐Ÿš€ Quick Start ### Deep Learning with TensorFlow ```markdown @tensorflow-keras-expert I need to build an image classifier for 10 classes: - Use transfer learning with MobileNetV2 - Fine-tune the top layers - Apply data augmentation - Use callbacks for early stopping and learning rate scheduling - Export to TensorFlow Lite for Android deployment ``` ### Deep Learning with PyTorch ```markdown @pytorch-expert Create a sentiment analysis model: - BERT-based architecture - Mixed precision training (FP16) - Distributed training on 2 GPUs with DDP - Gradient clipping for stability - Save best model based on validation F1 score ``` ### Reinforcement Learning ```markdown @reinforcement-learning-expert Train an RL agent to play Atari Pong: - Use DQN algorithm from Stable-Baselines3 - Frame stacking for temporal information - Reward clipping - Experience replay with prioritization - Evaluate every 10k steps - Stop when average reward exceeds 18 ``` ### Classical Machine Learning ```markdown @scikit-learn-expert Build a house price prediction model: - Features: numeric (area, bedrooms) and categorical (location, type) - Pipeline with imputation, scaling, and encoding - RandomizedSearchCV for hyperparameter tuning - Ridge regression with cross-validation - Feature importance and SHAP analysis ``` ## ๐Ÿ“‹ Agent Capabilities ### TensorFlow/Keras Agent **Strengths**: - Production deployment (TensorFlow Serving, TFLite) - TPU training for massive scale - Strong mobile/edge support - Rich ecosystem (TensorFlow Hub, TFX) **Use Cases**: - Mobile apps (TFLite) - Large-scale training (TPUs) - Production serving (TF Serving) - Browser deployment (TensorFlow.js) ### PyTorch Agent **Strengths**: - Research flexibility - Dynamic computational graphs - Excellent debugging - Strong community support **Use Cases**: - Research experiments - Custom architectures - Academic projects - Prototyping new models ### Reinforcement Learning Agent **Strengths**: - Game AI and robotics - Sequential decision making - Multi-agent systems - Adaptive systems **Use Cases**: - Game playing (Atari, board games) - Robotics control - Resource optimization - Recommendation systems ### Scikit-learn Agent **Strengths**: - Fast prototyping - Interpretable models - Small datasets - Production simplicity **Use Cases**: - Tabular data - Feature engineering - Quick baselines - Interpretable ML ## ๐Ÿงช Context7-Verified Patterns ### TensorFlow Patterns 1. โœ… Callbacks for checkpointing and early stopping 2. โœ… XLA compilation with `jit_compile=True` 3. โœ… Distributed training with MirroredStrategy 4. โœ… Mixed precision with `mixed_float16` policy 5. โœ… Proper data normalization (/ 255.0) 6. โœ… Learning rate schedules (ExponentialDecay, CosineAnnealing) 7. โœ… TensorFlow Lite conversion for mobile 8. โœ… Custom training loops with @tf.function 9. โœ… Model evaluation on test set 10. โœ… SavedModel format for deployment ### PyTorch Patterns 1. โœ… torch.compile for 2x speedup 2. โœ… DistributedDataParallel (DDP) for multi-GPU 3. โœ… Mixed precision with torch.amp.autocast 4. โœ… Proper nn.Module structure with super().__init__ 5. โœ… DataLoader with num_workers and pin_memory 6. โœ… Model checkpointing (state_dict, not full model) 7. โœ… Gradient clipping for stability 8. โœ… Learning rate schedulers (StepLR, CosineAnnealing) 9. โœ… Custom loss functions with autograd 10. โœ… Proper CUDA device management ### RL Patterns 1. โœ… Gymnasium API (observation, reward, terminated, truncated, info) 2. โœ… Epsilon-greedy exploration with decay 3. โœ… Q-Learning with Bellman equation updates 4. โœ… Custom environments with proper reset/step 5. โœ… Stable-Baselines3 with callbacks 6. โœ… Vectorized environments for speedup 7. โœ… Reward shaping (dense vs sparse rewards) 8. โœ… Early stopping on reward threshold 9. โœ… Model saving and loading 10. โœ… Custom training callbacks for monitoring ### Scikit-learn Patterns 1. โœ… Pipelines to prevent data leakage 2. โœ… Cross-validation with pipelines 3. โœ… GridSearchCV/RandomizedSearchCV for tuning 4. โœ… Feature selection as pipeline step 5. โœ… class_weight='balanced' for imbalanced data ## ๐Ÿ”ง Configuration ### Environment Variables ```bash # TensorFlow export TF_FORCE_GPU_ALLOW_GROWTH=true export TF_XLA_FLAGS=--tf_xla_enable_xla_devices # PyTorch export CUDA_VISIBLE_DEVICES=0,1 export TORCH_DISTRIBUTED_DEBUG=DETAIL # RL export GYM_RENDER_MODE=human ``` ## ๐Ÿ“– Documentation - [TensorFlow/Keras Expert Guide](./agents/tensorflow-keras-expert.md) - [PyTorch Expert Guide](./agents/pytorch-expert.md) - [RL Expert Guide](./agents/reinforcement-learning-expert.md) - [Scikit-learn Expert Guide](./agents/scikit-learn-expert.md) ## ๐Ÿค Contributing Contributions are welcome! Please see [CONTRIBUTING.md](../../CONTRIBUTING.md) for guidelines. ## ๐Ÿ“„ License MIT ยฉ OpenCodeAutoPM Team ## ๐Ÿ”— Links - [OpenCodeAutoPM](https://github.com/rafeekpro/OpenCodeAutoPM) - [Plugin Documentation](https://github.com/rafeekpro/OpenCodeAutoPM/blob/main/docs/PLUGIN-IMPLEMENTATION-PLAN.md) - [npm Package](https://www.npmjs.com/package/@claudeautopm/plugin-ml) - [Issues](https://github.com/rafeekpro/OpenCodeAutoPM/issues)