AI/ML Data Science Plugins - COMPLETE

AI/ML Data Science Plugins - COMPLETE

Date: October 11, 2025 Location: /home/jeremy/projects/claude-code-plugins/plugins/ai-ml/ 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:

/plugin marketplace add jeremylongshore/claude-code-plugins

Install individual plugins:

# 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

/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

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

Deploy a Model

/deploy-model
# Creates:
# - FastAPI REST API
# - Docker container
# - Health check endpoints
# - Batch prediction endpoint

Check AI Ethics

/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