Skill UI

A structured methodology for coordinating multiple, simultaneous investigation threads to determine the root cause of complex system failures, performance regressions, or integration bugs. This skill decomposes a large problem into independent tracks (e.g., database, application code, infrastructure), assigns ownership, and manages structured sync points (Continue/Pivot/Converge) to converge on a solution faster than serial debugging. Essential for high-stakes production incident response.

This tool generates a complete, production-ready PHP Model Context Protocol (MCP) server project. It automatically scaffolds the entire structure, including tools, resources, prompts, and comprehensive tests, utilizing the official PHP SDK. It provides a standardized, robust starting point for developers building AI-integrated backend services in PHP.

This comprehensive scaffold sets up a complete, modern Power Apps Code App project structure using Vite, React, and TypeScript. It guides developers through initializing the project with PAC CLI, configuring SDK integration, and setting up best practices for connecting to 1,500+ Power Platform connectors (e.g., Office 365, SharePoint, SQL Server). Ideal for building complex, code-first enterprise applications within the Power Platform ecosystem.

This tool acts as an expert prompt engineer, guiding users through a systematic discovery process to create highly structured, production-ready prompts for AI assistants like GitHub Copilot. It covers defining persona, specifying complex tasks, managing context variables, integrating tools, and enforcing strict output standards, ensuring the resulting prompt is comprehensive and actionable for advanced AI agents.

Scaffold a production-ready Python MCP server with uv-based tooling, FastMCP configuration, async tools, structured schemas, and comprehensive error handling plus deployment/testing guidance.

Analyzes Copilot guidance files and repository docs to craft structured README.md content, detailing the project purpose, architecture, technology stack, workflows, coding standards, testing approach, and contributing advice for fellow developers.

This skill provides comprehensive guidelines and techniques for safe code refactoring. It focuses on improving code structure, readability, and maintainability without altering the external behavior or functionality of the system. Learn to address common code smells, such as long methods, duplicated logic, god objects, and primitive obsession, using established design patterns and best practices. It emphasizes that refactoring should be gradual, test-driven, and methodical.

Transforms repo structure and commit history into two actionable markdowns—an architecture summary and a narrative story—so analysts can trace system intent, data flow, and team evolution.

Acts as a senior software engineer to meticulously review and refactor entire codebases. This skill ensures that the code is clean, highly maintainable, and strictly adheres to defined coding standards and best practices specified across multiple project instruction files. It guarantees the integrity of the existing file structure and ensures all unit tests remain passing after modifications.

Generates a complete, compilable xUnit integration test project structure for validating complex database migrations, specifically from Oracle to PostgreSQL, within .NET solutions. This scaffolding includes a transaction-rollback base class and a seed data manager, ensuring a robust environment for writing migration integration tests without requiring test cases. Ideal for setting up testing infrastructure before migration validation.

A comprehensive automation toolkit for managing Sentry error tracking and monitoring operations. Users can programmatically investigate issues, configure project-level alert rules, manage release versions, and monitor organizational structure. Essential for DevOps and development teams needing deep integration and programmatic control over error data.

A comprehensive tool for detecting, validating, and generating Schema.org structured data. It primarily focuses on the JSON-LD format, which is preferred for modern SEO best practices and enabling rich results. Use this when optimizing content markup, checking for validation errors, or generating structured snippets for articles, businesses, or products.