Skill UI

Copilot Spaces allow users to consolidate and manage project-specific knowledge, including documentation, code snippets, architectural standards, and operational instructions. By loading a Space, Copilot grounds its responses not just on general knowledge, but on the curated, authoritative context of the entire project, enabling accurate Q&A, code generation, and workflow execution based on team standards. Ideal for onboarding, large-scale projects, and maintaining consistency.

This guide outlines comprehensive best practices for documenting C# APIs using XML comments. It provides detailed rules for writing summaries, documenting parameters, return values, constructors, properties, and handling exceptions. Following these standards ensures that C# codebases are highly maintainable, readable, and easily understood by other developers.

The comprehensive command-line interface (CLI) for interacting with all major features of GitHub. This tool allows developers to manage repositories, create and review pull requests, track issues, utilize Codespaces, and perform advanced organizational tasks—all directly from the terminal. It is essential for streamlining DevOps workflows and automating GitHub operations.

This guide outlines comprehensive best practices for documenting Java code using Javadoc comments. It covers how to properly document public, protected, package-private, and private members, along with the correct usage of various tags like @param, @return, @throws, @see, and specialized syntax for code snippets and versioning. Following these standards ensures API documentation is accurate, maintainable, and adheres to professional Java development norms.

This guide provides comprehensive protocols and command structures for working with n8n-as-code. It covers workspace readiness, environment setup (remote or local), workflow authoring, validation, syncing, and troubleshooting using dedicated commands like `n8nac` and `n8n-manager`. It ensures structured, context-aware interaction with n8n workflows.

An advanced, AI-powered agent designed to generate comprehensive and workable unit tests for virtually any programming language (e.g., Python, C#, Java, Go, TypeScript). It operates via a sophisticated multi-agent pipeline (Research → Plan → Implement → Build → Test → Fix → Lint). This ensures generated tests are not only written following project conventions but also compile, pass all tests, and effectively improve code coverage.

This guide implements the Red-Green-Refactor cycle, the core methodology of Test-Driven Development (TDD). It teaches developers to first write a failing test (RED), then implement the minimum required code to make the test pass (GREEN), and finally improve the structure and clarity of the code without changing its behavior (REFACTOR). Use this guide when mastering TDD, ensuring robust unit test coverage, or following best practices for writing resilient software.

This advanced skill automates the enforcement of Python code quality using Ruff. It implements a controlled, iterative workflow that supports safe and unsafe automatic autofixes, formats the code, and reviews diffs after each pass. The process recursively repeats until all findings are resolved or require explicit user decision, ensuring high code consistency and quality gates.

A comprehensive guide detailing how to configure and manage GitHub's secret scanning feature. This skill covers enabling Secret Protection, setting up push protection to block leaked credentials before they reach the repository, defining custom regex patterns, and providing workflows for triaging alerts and resolving blocked pushes (including using git rebase or bypassing protections). Essential for maintaining secure code repositories and complying with security standards.

Provides patterns and techniques for building robust evaluation loops for AI agents. This skill guides the implementation of self-critique, evaluator-optimizer pipelines, and test-driven refinement workflows. Use it to significantly enhance the accuracy and quality of complex outputs like code, reports, and analyses by implementing structured, iterative improvement cycles (Generate → Evaluate → Critique → Refine).

A comprehensive tool that analyzes existing codebases to generate detailed, definitive architectural blueprints. It automatically detects technology stacks, identifies architectural patterns (e.g., Microservices, Clean Architecture), and documents implementation details, including dependency flow, cross-cutting concerns (security, logging), and data models. This ensures architectural consistency, aids in onboarding new team members, and guides future development efforts.

This comprehensive guide provides step-by-step instructions for setting up a development environment to use AWS CDK (Cloud Development Kit) with Python. It covers installing prerequisites (Node.js, AWS CLI), initializing a CDK project, managing Python dependencies, and mastering the full deployment workflow, enabling users to define and deploy scalable AWS infrastructure as code.