> browse/registry

> adapting-transfer-learning-models

This skill automates the adaptation of pre-trained machine learning models using transfer learning techniques. It is triggered when the user requests assistance with fine-tuning a model, adapting a pre-trained model to a new dataset, or performing transfer learning. It analyzes the user's requirements, generates code for adapting the model, includes data validation and error handling, provides performance metrics, and saves artifacts with documentation. Use this skill when you need to leverage e

> azure-copilot

Expert knowledge for Azure Copilot development including troubleshooting, decision making, architecture & design patterns, security, configuration, and integrations & coding patterns. Use when building, debugging, or optimizing Azure Copilot applications. Not for Azure AI services (use azure-ai-services), Azure Machine Learning (use azure-machine-learning), Azure Portal (use azure-portal), Azure AI Foundry Local (use azure-ai-foundry-local).

> azure-data-science-vm

Expert knowledge for Azure Data Science Virtual Machines development including troubleshooting, decision making, architecture & design patterns, security, configuration, integrations & coding patterns, and deployment. Use when building, debugging, or optimizing Azure Data Science Virtual Machines applications. Not for Azure Virtual Machines (use azure-virtual-machines), Azure Machine Learning (use azure-machine-learning), Azure Databricks (use azure-databricks), Azure HDInsight (use azure-hdinsi

> fine-tuning-with-trl

Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.

> detecting-data-anomalies

This skill empowers Claude to identify anomalies and outliers within datasets. It leverages the anomaly-detection-system plugin to analyze data, apply appropriate machine learning algorithms, and highlight unusual data points. Use this skill when the user requests anomaly detection, outlier analysis, or identification of unusual patterns in data. Trigger this skill when the user mentions "anomaly detection," "outlier analysis," "unusual data," or requests insights into data irregularities.

> preprocessing-data-with-automated-pipelines

This skill empowers Claude to preprocess and clean data using automated pipelines. It is designed to streamline data preparation for machine learning tasks, implementing best practices for data validation, transformation, and error handling. Claude should use this skill when the user requests data preprocessing, data cleaning, ETL tasks, or mentions the need for automated pipelines for data preparation. Trigger terms include "preprocess data", "clean data", "ETL pipeline", "data transformation"

> explaining-machine-learning-models

This skill enables Claude to provide interpretability and explainability for machine learning models. It is triggered when the user requests explanations for model predictions, insights into feature importance, or help understanding model behavior. The skill leverages techniques like SHAP and LIME to generate explanations. It is useful when debugging model performance, ensuring fairness, or communicating model insights to stakeholders. Use this skill when the user mentions "explain model", "inte

> azure-machine-learning

Expert knowledge for Azure Machine Learning development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building, debugging, or optimizing Azure Machine Learning applications. Not for Azure Data Science Virtual Machines (use azure-data-science-vm), Azure Databricks (use azure-databricks), Azure HDInsight (use azure-hdinsight), Azure Synapse Analytics (use

> tuning-hyperparameters

This skill enables Claude to optimize machine learning model hyperparameters using grid search, random search, or Bayesian optimization. It is used when the user requests hyperparameter tuning, model optimization, or improvement of model performance. The skill analyzes the current context, generates code for the specified search strategy, handles data validation and errors, and provides performance metrics. Trigger terms include "tune hyperparameters," "optimize model," "grid search," "random se

> setting-up-experiment-tracking

This skill automates the setup of machine learning experiment tracking using tools like MLflow or Weights & Biases (W&B). It is triggered when the user requests to "track experiments", "setup experiment tracking", "initialize MLflow", or "integrate W&B". The skill configures the necessary environment, initializes the tracking server (if needed), and provides code snippets for logging experiment parameters, metrics, and artifacts. It helps ensure reproducibility and simplifies the comparison of d

> azure-ai-services

Expert knowledge for Azure AI services development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building, debugging, or optimizing Azure AI services applications. Not for Azure AI Vision (use azure-ai-vision), Azure AI Anomaly Detector (use azure-anomaly-detector), Azure AI Search (use azure-cognitive-search), Azure Machine Learning (use azure-machine-learning).

> llama-factory

Expert guidance for fine-tuning LLMs with LLaMA-Factory - WebUI no-code, 100+ models, 2/3/4/5/6/8-bit QLoRA, multimodal support

> azure-open-datasets

Expert knowledge for Azure Open Datasets development including limits & quotas. Use when building, debugging, or optimizing Azure Open Datasets applications. Not for Azure Data Explorer (use azure-data-explorer), Azure Synapse Analytics (use azure-synapse-analytics), Azure Databricks (use azure-databricks), Azure Machine Learning (use azure-machine-learning).

> aeon

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

> pyvene-interventions

Provides guidance for performing causal interventions on PyTorch models using pyvene's declarative intervention framework. Use when conducting causal tracing, activation patching, interchange intervention training, or testing causal hypotheses about model behavior.

> azure-ai-foundry-local

Expert knowledge for Azure AI Foundry Local development including troubleshooting, best practices, decision making, configuration, and integrations & coding patterns. Use when building, debugging, or optimizing Azure AI Foundry Local applications. Not for Azure AI services (use azure-ai-services), Azure Machine Learning (use azure-machine-learning), Azure AI Vision (use azure-ai-vision), Azure AI Document Intelligence (use azure-document-intelligence).

> grpo-rl-training

Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training

> azure-custom-vision

Expert knowledge for Azure AI Custom Vision development including best practices, decision making, limits & quotas, security, integrations & coding patterns, and deployment. Use when building, debugging, or optimizing Azure AI Custom Vision applications. Not for Azure AI Vision (use azure-ai-vision), Azure AI services (use azure-ai-services), Azure Machine Learning (use azure-machine-learning), Azure AI Search (use azure-cognitive-search).

> azure-databricks

Expert knowledge for Azure Databricks development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building, debugging, or optimizing Azure Databricks applications. Not for Azure HDInsight (use azure-hdinsight), Azure Synapse Analytics (use azure-synapse-analytics), Azure Machine Learning (use azure-machine-learning), Azure Data Explorer (use azure-data-ex

> gtars

High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.