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 different model runs.

This skill optimizes deep learning models using various techniques. It is triggered when the user requests improvements to model performance, such as increasing accuracy, reducing training time, or minimizing resource consumption. The skill leverages advanced optimization algorithms like Adam, SGD, and learning rate scheduling. It analyzes the existing model architecture, training data, and performance metrics to identify areas for enhancement. The skill then automatically applies appropriate optimization strategies and generates optimized code. Use this skill when the user mentions "optimize deep learning model", "improve model accuracy", "reduce training time", or "optimize learning rate".

This skill enables Claude to split datasets into training, validation, and testing sets. It is useful when preparing data for machine learning model development. Use this skill when the user requests to split a dataset, create train-test splits, or needs data partitioning for model training. The skill is triggered by terms like "split dataset," "train-test split," "validation set," or "data partitioning."

This skill enables Claude to generate data visualizations, plots, charts, and graphs from provided data. It analyzes the data, selects the most appropriate visualization type, and creates a visually appealing and informative graphic. Use this skill when the user requests a visualization, plot, chart, or graph; when data needs to be presented visually; or when exploring data patterns. The skill is triggered by requests for "visualization", "plot", "chart", or "graph".

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", and "data validation". The skill ensures data quality and prepares it for effective analysis and model training.

This skill enables Claude to process and analyze images using computer vision techniques. It's used to perform tasks such as object detection, image classification, and image segmentation. Use this skill when a user requests analysis of an image, asks for identification of objects within an image, or needs help with other computer vision related tasks. Trigger terms include "analyze image", "object detection", "image classification", "image segmentation", "computer vision", "process image", or when the user provides an image and asks for insights.

This skill enables Claude to execute clustering algorithms on datasets. It is used when the user requests to perform clustering, identify groups within data, or analyze data structure. The skill supports algorithms like K-means, DBSCAN, and hierarchical clustering. Claude should use this skill when the user explicitly asks to "run clustering," "perform a cluster analysis," or "group data points" and provides a dataset or a way to access one. The skill also handles data validation, error handling, performance metrics, and artifact saving.

This skill enables Claude to construct and evaluate classification models using provided datasets or specifications. It leverages the classification-model-builder plugin to automate model creation, optimization, and reporting. Use this skill when the user requests to "build a classifier", "create a classification model", "train a classification model", or needs help with supervised learning tasks involving labeled data. The skill ensures best practices are followed, including data validation, error handling, and performance metric reporting.

This skill empowers Claude to build AutoML pipelines using the automl-pipeline-builder plugin. It is triggered when the user requests the creation of an automated machine learning pipeline, specifies the use of AutoML techniques, or asks for assistance in automating the machine learning model building process. The skill analyzes the context, generates code for the ML task, includes data validation and error handling, provides performance metrics, and saves artifacts with documentation. Use this skill when the user explicitly asks to "build automl pipeline", "create automated ml pipeline", or needs help with "automating machine learning workflows".

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.

This skill enables Claude to orchestrate multi-agent systems using the AI SDK v5. It allows Claude to set up agent handoffs, intelligent routing, and coordinated workflows across different AI providers like OpenAI, Anthropic, and Google. Use this skill when the user asks to create multi-agent systems, needs help with agent coordination, task routing, or wants to build complex workflows involving specialized agents. It is triggered by phrases like "multi-agent system", "agent orchestration", "agent handoff", "intelligent routing", or "coordinate agents".

This skill enables Claude to validate the ethical implications and fairness of AI/ML models and datasets. It is triggered when the user requests an ethics review, fairness assessment, or bias detection for an AI system. The skill uses the ai-ethics-validator plugin to analyze models, datasets, and code for potential biases and ethical concerns. It provides reports and recommendations for mitigating identified issues, ensuring responsible AI development and deployment. Use this skill when the user mentions "ethics validation", "fairness assessment", "bias detection", "responsible AI", or related terms in the context of AI/ML.

This skill enables Claude to execute visual regression tests using tools like Percy, Chromatic, and BackstopJS. It captures screenshots, compares them against baselines, and analyzes visual differences to identify unintended UI changes. Use this skill when the user requests visual testing, UI change verification, or regression testing for a web application or component. Trigger phrases include "visual test," "UI regression," "check visual changes," or "/visual-test".

This skill enables Claude to automatically generate comprehensive unit tests from source code. It is triggered when the user requests unit tests, test cases, or test suites for specific files or code snippets. The skill supports multiple testing frameworks including Jest, pytest, JUnit, and others, intelligently detecting the appropriate framework or using one specified by the user. Use this skill when the user asks to "generate tests", "create unit tests", or uses the shortcut "gut" followed by a file path.

This skill generates comprehensive test reports with coverage metrics, trends, and stakeholder-friendly formats (HTML, PDF, JSON). It aggregates test results from various frameworks, calculates key metrics (coverage, pass rate, duration), and performs trend analysis. Use this skill when the user requests a test report, coverage analysis, failure analysis, or historical comparisons of test runs. Trigger terms include "test report", "coverage report", "testing trends", "failure analysis", and "historical test data".

This skill enables Claude to orchestrate complex test workflows using the test-orchestrator plugin. It allows Claude to define test execution graphs with dependencies, execute tests in parallel, and intelligently select tests to run based on code changes. Use this skill when a user requests test orchestration, dependency management for tests, parallel test execution, smart test selection, or CI/CD integration for testing. Trigger terms include "orchestrate tests", "test workflow", "parallel testing", "smart test selection", "test dependencies", and "/orchestrate".

This skill enables Claude to manage isolated test environments using Docker Compose, Testcontainers, and environment variables. It is used to create consistent, reproducible testing environments for software projects. Claude should use this skill when the user needs to set up a test environment with specific configurations, manage Docker Compose files for test infrastructure, set up programmatic container management with Testcontainers, manage environment variables for tests, or ensure cleanup after tests. Trigger terms include "test environment", "docker compose", "testcontainers", "environment variables", "isolated environment", "env-setup", and "test setup".

This skill uses the test-doubles-generator plugin to automatically create mocks, stubs, spies, and fakes for unit testing. It analyzes dependencies in the code and generates appropriate test doubles based on the chosen testing framework, such as Jest, Sinon, or others. Use this skill when you need to generate test doubles, mocks, stubs, spies, or fakes to isolate units of code during testing. Trigger this skill by requesting test double generation or using the `/gen-doubles` or `/gd` command.

This skill enables Claude to generate realistic test data for software development. It uses the test-data-generator plugin to create users, products, orders, and custom schemas for comprehensive testing. Use this skill when you need to populate databases, simulate user behavior, or create fixtures for automated tests. Trigger phrases include "generate test data", "create fake users", "populate database", "generate product data", "create test orders", or "generate data based on schema". This skill is especially useful for populating testing environments or creating sample data for demonstrations.

This skill analyzes code coverage metrics to identify untested code and generate comprehensive coverage reports. It is triggered when the user requests analysis of code coverage, identification of coverage gaps, or generation of coverage reports. The skill is best used to improve code quality by ensuring adequate test coverage and identifying areas for improvement. Use trigger terms like "analyze coverage", "code coverage report", "untested code", or the shortcut "cov".