Skill UI

Streamlines the process of providing code review feedback by enforcing a highly structured, ultra-compressed format. It guides users to cut through noise and deliver actionable, single-line comments (Location: Problem. Fix.) while maintaining critical context like severity and technical rationale. Ideal for team settings prioritizing efficiency and high code quality.

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 skill guides the generation of a complete, production-ready Model Context Protocol (MCP) server using C# and .NET. It covers advanced architectural patterns, including Host builder setup, proper dependency injection, structured logging, and implementing robust functional tools. Ideal for developers building sophisticated AI agents or backend services that require standardized communication and tool invocation capabilities.

This comprehensive guide provides best practices for developing robust and efficient data access layers using Entity Framework Core. It covers critical areas including DbContext design, performance optimization (e.g., AsNoTracking, Select projection), secure querying techniques, migration strategies, and testing methodologies, ensuring the creation of scalable and maintainable applications.

A comprehensive workflow for discovering, integrating, and utilizing third-party APIs and SDKs. This skill guides the process by first detecting the project language, ensuring necessary guidelines and skills are present, systematically fetching API details, and providing structured guidance on authentication, model usage, and endpoint implementation. It tracks integration progress by recording specific development milestones (e.g., SDK setup, first call made).

This skill provides an interactive, guided onboarding tour for the Context-Matic MCP server. It addresses the challenge of general AI models relying on outdated or incorrect public code by acting as a live, version-aware grounding layer. The tour walks users through the server's core function, demonstrates key tools (like fetching APIs, searching models, and finding endpoints), and guides them through selecting and exploring available APIs for their specific project language.

A powerful system for organizing and tracking complex, multi-step projects, research, and workflows. It utilizes persistent Markdown files (task_plan.md, findings.md, progress.md) as an external, persistent working memory. This ensures that all critical context, findings, and progress updates are reliably recorded, preventing information loss and maintaining continuity across sessions. Ideal for detailed project management and deep research work.

This guide provides the complete, step-by-step migration pattern for outdated React context APIs (such as contextTypes, childContextTypes, and getChildContext) to the modern createContext API. Since context migration is always a cross-file process, this skill emphasizes the required coordination between the provider and all consumer components to prevent runtime failures and correctly update the entire application codebase.

This tool generates a complete, production-ready project scaffold for a Model Context Protocol (MCP) server written in Ruby. It includes the necessary file structure, gem dependencies, Rake tasks, and boilerplate code for defining tools, prompts, and resources, accelerating backend development for AI applications.

Transforms existing code files into highly effective learning resources by adding context-aware educational comments. Acting as an expert educator, this tool tailors explanations—from foundational concepts for beginners to deep architectural insights for advanced practitioners—while strictly maintaining the original file's structure, encoding, and syntax. Ideal for generating self-documenting, pedagogically rich codebases.

This guide outlines the structure and content guidelines for an AGENTS.md file. This file serves as a comprehensive 'README for agents,' providing detailed, technical context and operational instructions (such as setup, development workflow, testing procedures, and code style) that enable advanced AI coding agents to interact with and understand a codebase effectively. It greatly complements traditional READMEs by focusing purely on machine-readable technical requirements.

Analyzes your sent messages via WorkIQ to mirror your tone, structure, and sign-offs, then drafts or revises professional emails for follow-ups, proposals, replies, and other contexts so you can review and send polished copies.