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Backtesting Trading Strategies Framework

backtesting-trading-strategies

jeremylongshore/claude-code-plugins-plus-skills

This comprehensive framework allows users to validate and test both crypto and traditional trading strategies against extensive historical market data. It calculates essential performance metrics (Sharpe Ratio, Sortino Ratio, Max Drawdown), simulates trade execution, generates equity curves, and includes advanced parameter grid search optimization tools for robust strategy validation.

Classification Model Builder

building-classification-models

jeremylongshore/claude-code-plugins-plus-skills

Automates building and evaluating classification models by analyzing datasets, selecting features, tuning hyperparameters, and reporting metrics via the classification-model-builder plugin for supervised learning requests.

AutoML Pipeline Builder

building-automl-pipelines

jeremylongshore/claude-code-plugins-plus-skills

Builds AutoML pipelines by analyzing user requests, generating code for data preparation, model selection, hyperparameter tuning, validation, and metrics, then documenting artifacts for streamlined automated machine learning workflows.

Optuna Study Creator

optuna-study-creator

jeremylongshore/claude-code-plugins-plus-skills

Auto-activating ML training skill that kicks in whenever you mention optuna study creator, then guides you through data prep, model training, hyperparameter tuning, and experiment tracking with production-ready code and validations.

Ray Train Orchestration

Orchestra-Research/AI-Research-SKILLs

Ray Train orchestrates distributed PyTorch, TensorFlow, and HuggingFace workloads from laptops to clusters, automating GPU placement, fault tolerance, checkpointing, and hyperparameter sweeps so teams can scale massive model training with minimal code changes.

Sklearn Pipeline Builder

sklearn-pipeline-builder

jeremylongshore/claude-code-plugins-plus-skills

Automates sklearn pipeline builder tasks within ML training, activating on relevant triggers to guide data prep, model training, hyperparameter tuning, and experiment tracking with production-ready code and standards validation.

Tensorflow Model Trainer

tensorflow-model-trainer

jeremylongshore/claude-code-plugins-plus-skills

Auto-activating skill that guides tensorflow model trainer workflows, covering data preparation, model training, hyperparameter tuning, and experiment tracking with production-ready code and standards validation.

Machine Learning Training

training-machine-learning-models

jeremylongshore/claude-code-plugins-plus-skills

Automates the end-to-end training of machine learning models by analyzing datasets, selecting suitable algorithms, tuning parameters, performing cross-validated training, and emitting performance metrics plus a saved model artifact.

BRENDA Enzyme Explorer

brenda-database

K-Dense-AI/claude-scientific-skills

Access the BRENDA SOAP API to retrieve enzyme kinetic parameters, reaction equations, organism-specific data, and environmental preferences, enabling biochemical research, metabolic pathway reconstruction, and enzyme engineering workflows.

Scikit Learn Toolkit

K-Dense-AI/claude-scientific-skills

Guides developers through scikit-learn workflows for classical supervised and unsupervised tasks, data preprocessing, evaluation schemes, hyperparameter tuning, and end-to-end pipelines when building interpretable production models.

Analyze Machine Learning Experiment Results

analyze-results

wanshuiyin/Auto-claude-code-research-in-sleep

This tool analyzes ML experiment results from various paths (JSON/CSV). It computes comprehensive statistics, builds detailed comparison tables based on model types and hyperparameters, and generates actionable insights. Use it when you need to interpret raw experimental data, compare different model runs, or determine the next steps for research.

UMAP Nonlinear Dimensionality Reduction

K-Dense-AI/scientific-agent-skills

Use UMAP-learn for fast, scalable nonlinear dimensionality reduction, 2D/3D visualization embeddings, clustering preprocessing, supervised and semi-supervised UMAP, DensMAP, AlignedUMAP, and Parametric UMAP workflows, with guidance on key parameter tuning.

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