# AI/ML Data Science Plugins - COMPLETE

**Date:** October 11, 2025
**Location:** `
**Status:**  25/25 Plugins Created - MISSION COMPLETE

## Overview

Created **25 comprehensive AI/ML data science plugins** covering the complete machine learning lifecycle from data preprocessing to model deployment and ethical validation.

## Complete Plugin List

### Model Development (6 plugins)

1. **ml-model-trainer**
   - Train and optimize ML models with automated workflows
   - Cross-validation, performance metrics
   - Multi-framework support (scikit-learn, PyTorch, TensorFlow, XGBoost)
   - Command: `/train`

2. **neural-network-builder**
   - Build and configure neural network architectures
   - Layer design, activation functions
   - PyTorch/TensorFlow support
   - Command: `/build-nn`

3. **hyperparameter-tuner**
   - Grid search, random search, Bayesian optimization
   - Automated hyperparameter tuning
   - Integration with Optuna, Ray Tune
   - Command: `/tune-hyper`

4. **classification-model-builder**
   - Build classification models
   - Binary and multi-class classification
   - Feature importance analysis
   - Command: `/build-classifier`

5. **regression-analysis-tool**
   - Linear, polynomial, ridge, lasso regression
   - Residual analysis
   - Feature correlation
   - Command: `/run-regression`

6. **deep-learning-optimizer**
   - Adam, SGD, RMSprop optimizers
   - Learning rate scheduling
   - Gradient clipping
   - Command: `/optimize-dl`

### Data Processing (3 plugins)

7. **data-preprocessing-pipeline**
   - Automated data cleaning
   - Missing value handling
   - Feature scaling and normalization
   - Outlier detection
   - Command: `/preprocess`

8. **feature-engineering-toolkit**
   - Feature creation and transformation
   - Encoding categorical variables
   - Feature selection algorithms
   - Polynomial features
   - Command: `/feature-eng`

9. **dataset-splitter**
   - Train/validation/test splits
   - Stratified sampling
   - K-fold cross-validation
   - Time series splitting
   - Command: `/split-data`

### Domain-Specific ML (4 plugins)

10. **nlp-text-analyzer**
    - Text preprocessing and tokenization
    - Named entity recognition
    - POS tagging
    - Word embeddings (Word2Vec, GloVe, BERT)
    - Command: `/analyze-text`

11. **computer-vision-processor**
    - Image preprocessing
    - Object detection
    - Image classification
    - OpenCV, PIL integration
    - Command: `/process-vision`

12. **time-series-forecaster**
    - ARIMA, Prophet, LSTM models
    - Seasonal decomposition
    - Trend analysis
    - Forecasting with confidence intervals
    - Command: `/forecast-ts`

13. **recommendation-engine**
    - Collaborative filtering
    - Content-based filtering
    - Hybrid recommendation systems
    - Matrix factorization
    - Command: `/build-recommender`

### Analysis & Detection (4 plugins)

14. **anomaly-detection-system**
    - Isolation Forest
    - One-class SVM
    - Statistical methods
    - Time series anomaly detection
    - Command: `/detect-anomaly`

15. **sentiment-analysis-tool**
    - Polarity detection
    - Emotion classification
    - Aspect-based sentiment
    - VADER, TextBlob, transformers
    - Command: `/analyze-sentiment`

16. **clustering-algorithm-runner**
    - K-means, DBSCAN, hierarchical clustering
    - Elbow method for optimal K
    - Cluster visualization
    - Silhouette analysis
    - Command: `/run-clustering`

17. **model-evaluation-suite**
    - Accuracy, precision, recall, F1-score
    - ROC curves, AUC
    - Confusion matrices
    - Cross-validation metrics
    - Command: `/eval-model`

### MLOps & Production (4 plugins)

18. **model-deployment-helper**
    - Flask/FastAPI API creation
    - Docker containerization
    - Model serving with TorchServe, TF Serving
    - Batch prediction
    - Command: `/deploy-model`

19. **model-versioning-tracker**
    - MLflow integration
    - Model registry
    - Experiment tracking
    - Version comparison
    - Command: `/track-versions`

20. **experiment-tracking-setup**
    - MLflow, Weights & Biases setup
    - Parameter logging
    - Artifact management
    - Experiment comparison
    - Command: `/track-experiments`

21. **data-visualization-creator**
    - Matplotlib, Seaborn, Plotly
    - Distribution plots
    - Correlation heatmaps
    - Interactive dashboards
    - Command: `/viz-data`

### Advanced ML (4 plugins)

22. **automl-pipeline-builder**
    - H2O AutoML
    - TPOT, Auto-sklearn
    - Automated feature engineering
    - Model selection and tuning
    - Command: `/build-automl`

23. **transfer-learning-adapter**
    - Fine-tuning pre-trained models
    - Feature extraction
    - Domain adaptation
    - BERT, ResNet, VGG adapters
    - Command: `/adapt-transfer`

24. **model-explainability-tool**
    - SHAP values
    - LIME explanations
    - Feature importance
    - Partial dependence plots
    - Command: `/explain-model`

25. **ai-ethics-validator**
    - Bias detection
    - Fairness metrics
    - Responsible AI checks
    - Demographic parity
    - Command: `/validate-ethics`

## Installation

Add the marketplace to Claude Code:

```bash
/plugin marketplace add jeremylongshore/claude-code-plugins
```

Install individual plugins:

```bash
# Model training
/plugin install ml-model-trainer@claude-code-plugins-plus

# Data preprocessing
/plugin install data-preprocessing-pipeline@claude-code-plugins-plus

# NLP
/plugin install nlp-text-analyzer@claude-code-plugins-plus

# MLOps
/plugin install model-deployment-helper@claude-code-plugins-plus

# Ethics
/plugin install ai-ethics-validator@claude-code-plugins-plus
```

## Usage Examples

### Train a Model
```bash
/train
# Claude will guide you through:
# 1. Data loading and validation
# 2. Model selection
# 3. Training with cross-validation
# 4. Evaluation metrics
# 5. Model persistence
```

### Preprocess Data
```bash
/preprocess
# Automated pipeline:
# - Missing value handling
# - Outlier detection
# - Feature scaling
# - Encoding categorical variables
```

### Deploy a Model
```bash
/deploy-model
# Creates:
# - FastAPI REST API
# - Docker container
# - Health check endpoints
# - Batch prediction endpoint
```

### Check AI Ethics
```bash
/validate-ethics
# Validates:
# - Bias in training data
# - Fairness across demographics
# - Responsible AI practices
# - Explainability requirements
```

## Technical Stack

### Supported Frameworks
- **ML:** scikit-learn, XGBoost, LightGBM
- **Deep Learning:** PyTorch, TensorFlow, Keras
- **NLP:** NLTK, spaCy, Hugging Face Transformers
- **Computer Vision:** OpenCV, PIL, torchvision
- **MLOps:** MLflow, Weights & Biases, DVC
- **Deployment:** Flask, FastAPI, Docker

### Python Version
- Python 3.8+

### Key Libraries
- pandas, numpy, scipy
- matplotlib, seaborn, plotly
- scikit-learn, statsmodels
- torch, tensorflow
- transformers, spacy
- mlflow, wandb

## File Structure

Each plugin contains:

```
plugin-name/
├── .claude-plugin/
│   └── plugin.json          # Plugin metadata
├── commands/
│   └── command-name.md      # Slash command definition
├── scripts/                 # Helper scripts (optional)
├── README.md               # Documentation
└── LICENSE                 # MIT License
```

## Features

### Automation
- Automated data preprocessing pipelines
- Hyperparameter optimization
- Model selection and evaluation
- Deployment workflows

### Best Practices
- Cross-validation
- Feature engineering
- Model versioning
- Experiment tracking
- Ethical AI validation

### Integration
- Multi-framework support
- Cloud deployment ready
- Docker containerization
- API generation

### Monitoring
- Performance metrics
- Model drift detection
- Explainability tools
- Bias detection

## Category Statistics

- **Total Plugins:** 25
- **Commands:** 25
- **Categories:** 6
  - Model Development: 6
  - Data Processing: 3
  - Domain-Specific ML: 4
  - Analysis & Detection: 4
  - MLOps & Production: 4
  - Advanced ML: 4

## Quality Assurance

All plugins include:
-  Valid plugin.json metadata
-  Comprehensive README
-  MIT License
-  Slash command definition
-  Clear usage instructions
-  Framework compatibility
-  Error handling guidance

## Use Cases

### Data Science Workflow
1. **Data Prep:** data-preprocessing-pipeline
2. **Feature Engineering:** feature-engineering-toolkit
3. **Model Training:** ml-model-trainer
4. **Evaluation:** model-evaluation-suite
5. **Deployment:** model-deployment-helper
6. **Monitoring:** experiment-tracking-setup

### NLP Pipeline
1. **Text Analysis:** nlp-text-analyzer
2. **Sentiment:** sentiment-analysis-tool
3. **Model Training:** classification-model-builder
4. **Deployment:** model-deployment-helper
5. **Ethics:** ai-ethics-validator

### Computer Vision Pipeline
1. **Image Processing:** computer-vision-processor
2. **Model Building:** neural-network-builder
3. **Transfer Learning:** transfer-learning-adapter
4. **Evaluation:** model-evaluation-suite
5. **Deployment:** model-deployment-helper

### MLOps Workflow
1. **Training:** ml-model-trainer
2. **Tracking:** experiment-tracking-setup
3. **Versioning:** model-versioning-tracker
4. **Deployment:** model-deployment-helper
5. **Monitoring:** model-explainability-tool

## Future Enhancements

Potential additions:
- Reinforcement learning toolkit
- Federated learning support
- Edge device deployment
- Real-time inference optimization
- Multi-modal learning tools

## Contributing

Contributions welcome! See CONTRIBUTING.md for guidelines.

## License

All plugins are licensed under MIT License.

## Support

- **Repository:** https://github.com/jeremylongshore/claude-code-plugins
- **Issues:** https://github.com/jeremylongshore/claude-code-plugins/issues
- **Email:** [email protected]

---

**Status:**  COMPLETE - 25/25 Plugins Created
**Date:** October 11, 2025
**Author:** Jeremy Longshore
**Category:** AI/ML Data Science
