Skill UI

This skill provides expert-level control over a live Chrome browser. It enables full web automation (clicking, form filling, navigation), deep debugging (analyzing console logs, network requests), visual inspection (screenshots, snapshots), and detailed performance profiling. Ideal for QA engineers, web scraping, and front-end development testing.

This skill analyzes an entire .NET solution (containing multiple projects) to map out a systematic migration path from Oracle to PostgreSQL. It scans for common Oracle indicators (NuGet packages, connection strings, code usage) and classifies every project into categories like MIGRATE, SKIP, or ALREADY_MIGRATED. The output is a structured, actionable Master Migration Plan, helping development teams manage large-scale database transitions efficiently.

This skill guides the end-to-end process of building automated evaluation pipelines for Python applications powered by Large Language Models (LLMs). It teaches how to define evaluation criteria, instrument the application, build golden datasets, and run real-world evaluations using tools like pixie, ensuring the app's logic (not just the LLM) is rigorously tested. Ideal for QA, benchmarking, and improving LLM-based services.

This skill focuses on the 'Remove Parameter' refactoring pattern specific to Java development. It teaches systematic techniques to analyze methods and identify unused or redundant parameters. By applying this refactoring, developers can significantly improve code readability, enhance maintainability, reduce coupling, and simplify method signatures while preserving original functionality and logic.

A comprehensive guide for creating, updating, refactoring, and reviewing solutions built on the Microsoft Agent Framework. This skill provides unified best practices for both .NET and Python environments, serving as a modern successor to Semantic Kernel and AutoGen. It emphasizes adherence to the latest official documentation, async patterns, robust error handling, and structured agent/workflow orchestration for complex AI applications.

This skill provides structured procedures for managing NuGet packages in .NET projects and solutions. It strictly enforces the use of the `dotnet` CLI for adding, removing, and updating dependencies, prioritizing project integrity. It guides users through a mandatory workflow for version updates, including version verification and running `dotnet restore`, ensuring a stable and consistent development environment.

This skill allows users to generate structured and composed UI patterns—such as card sections, multi-column grids, forms, and data tables—by combining established design system primitives. It ensures consistency and reusability by adhering to defined layout rules, making complex UI development systematic and efficient.

This skill guides the systematic execution of complex development plans. It mandates a critical review of the plan for dependencies, ambiguous conditions, and environmental assumptions before execution. The process involves incremental task completion, rigorous testing, structured committing, and final reporting, ensuring adherence to established development best practices.

This skill guides developers through the structured completion of a feature branch. It enforces running comprehensive tests, detects the current Git environment (e.g., common repository vs. worktree), and presents clear options (merge, PR, retain, discard). It ensures safe integration, manages worktree cleanup, and enforces best practices for version control workflows.

A powerful utility for performing semantic searches across a large knowledge base of 105K+ skill cards. It allows users to find the most relevant skills based on natural language queries, supporting both Python API integration and Command Line Interface (CLI). This tool is zero-dependency and highly efficient for skill discovery and knowledge retrieval in development workflows.

This skill provides comprehensive tools for developing production-grade Python Protocol API v2 protocols for Opentrons Flex and OT-2 robots. It covers the entire automated scientific workflow, including precise hardware configuration, labware setup, liquid handling, module control, local simulation, and strict safety deployment procedures. Essential for automating complex, high-throughput benchtop tasks.

This advanced skill optimizes LLM context usage by implementing strict rules for prompt caching, efficient log pruning, AST-based code inspection, and compact data structuring. It minimizes token consumption and maximizes information density, suitable for highly technical and iterative development workflows.