The package manager for agent skills and context

This skill empowers Claude to perform feature engineering tasks for machine learning. It creates, selects, and transforms features to improve model performance. Use this skill when the user requests feature creation, feature selection, feature transformation, or any request that involves improving the features used in a machine learning model. Trigger terms include "feature engineering", "feature selection", "feature transformation", "create features", "select features", "transform features", "improve model performance", and similar phrases related to feature manipulation.

Automatically manages PostgreSQL backups with pgBackRest and Wasabi S3 storage when working with FairDB databases

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 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 enables Claude to monitor and analyze application error rates to improve reliability. It is used when the user needs to track and understand errors occurring in their application, including HTTP errors, application exceptions, database errors, external API errors, background job errors, and client-side errors. Use this skill when the user asks to "monitor errors", "analyze error rates", "track application errors", or requests help with "error monitoring". It sets up comprehensive error tracking and alerting based on defined thresholds.

This skill enables Claude to generate end-to-end (E2E) tests for web applications. It leverages Playwright, Cypress, or Selenium to automate browser interactions and validate user workflows. Use this skill when the user requests to "create E2E tests", "generate end-to-end tests", or asks for help with "browser-based testing". The skill is particularly useful for testing user registration, login flows, shopping cart functionality, and other multi-step processes within a web application. It supports cross-browser testing and can be used to verify the responsiveness of web applications on different devices.

This skill enables Claude to generate end-to-end (E2E) tests for web applications. It leverages Playwright, Cypress, or Selenium to automate browser interactions and validate user workflows. Use this skill when the user requests to "create E2E tests", "generate end-to-end tests", or asks for help with "browser-based testing". The skill is particularly useful for testing user registration, login flows, shopping cart functionality, and other multi-step processes within a web application. It supports cross-browser testing and can be used to verify the responsiveness of web applications on different devices.

This skill enables Claude to generate Docker Compose configurations for multi-container applications. It leverages best practices for production-ready deployments, including defining services, networks, volumes, health checks, and resource limits. Claude should use this skill when the user requests a Docker Compose file, specifies application architecture involving multiple containers, or mentions needs for container orchestration, environment variables, or persistent data management in a Docker environment. Trigger terms include "docker-compose", "docker compose file", "multi-container", "container orchestration", "docker environment", "service definition", "volume management", "network configuration", "health checks", "resource limits", and ".env files".

Optimize user CLAUDE.md files by applying progressive disclosure principles. This skill should be used when users want to reduce CLAUDE.md bloat, move detailed content to references, extract reusable patterns into skills, or improve context efficiency. Triggers include "optimize CLAUDE.md", "reduce CLAUDE.md size", "apply progressive disclosure", or complaints about CLAUDE.md being too long.

This skill enables Claude to plan and implement disaster recovery (DR) procedures. It generates configurations and setup code based on specific requirements and infrastructure. Use this skill when the user requests assistance with disaster recovery planning, business continuity, or related DevOps tasks. Trigger this skill when the user mentions "disaster recovery", "DR plan", "business continuity", or requests help with setting up a recovery strategy. It provides production-ready configurations, implements best practices, and supports multi-platform environments.

This skill manages and executes deployment rollbacks with safety checks. It helps ensure smooth recovery from failed deployments by automating the rollback process. Use this skill when a deployment has issues, such as errors, performance degradation, or unexpected behavior. The skill is triggered by requests to "rollback deployment", "revert to previous version", or similar phrases related to deployment recovery. It prioritizes safe rollback procedures and provides options for verification.

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".