Skill UI

The official SDK for building applications that programmatically interact with GitHub Copilot. It provides comprehensive features including session management, custom tool definition (function calling), real-time streaming, and lifecycle hooks. Supports multiple languages, including Node.js, Python, Go, and .NET, making AI integration seamless across your tech stack.

This skill guides users through structured workshops to define comprehensive product features from scratch. It adopts dual perspectives (Product Manager and Developer) to gather detailed requirements, user stories, and non-functional needs. The output includes EARS-formatted functional requirements, testable acceptance criteria (Given/When/Then), and detailed implementation checklists, ensuring the feature is complete and ready for development.

This skill generates complete gRPC service definitions, client/server stubs, and robust implementations directly from Protocol Buffer (.proto) files. It supports all streaming patterns (unary, server, client, bidirectional) and includes advanced features like authentication interceptors, health checks, and automatic REST-to-gRPC gateway generation, making it ideal for building high-performance, enterprise-grade microservices.

This skill orchestrates the entire coding lifecycle as a state machine, ensuring tasks proceed from issue intake through implementation, structured review, deployment, and final acceptance verification. It enforces a rigorous 'Definition of Done' (DoD) proof, moving beyond simple code changes. Ideal for complex feature development requiring structured handoffs and explicit verification evidence.

This advanced prompt simulates a Senior Project Manager and DevOps specialist. It takes comprehensive feature artifacts (PRD, technical breakdowns, testing plans) and generates a structured, automated GitHub project plan. It enforces Agile best practices (Epic > Feature > Story > Enabler > Task) and includes templates for issue creation, dependency management, risk assessment, and Definition of Done, ensuring robust project tracking within GitHub.

This skill analyzes the implementation gap by comparing approved feature requirements against the existing codebase, architecture, and context. It helps identify missing capabilities, potential integration challenges, and necessary technical research areas. The output provides multiple viable implementation options and a comprehensive analysis report, guiding the transition from requirements definition to technical design.

Automatically scaffolds the initial project specification and requirements documents (spec.json and requirements.md). It guides the user through scope definition by checking existing project briefs, clarifying intent (who, current state, desired change), and generating a structured, ready-to-use foundation for development planning. Ideal for starting any new feature or project.

This module provides essential abstractions and hooks (BuildParam, BuildParams, useBuildParams) for managing build parameters within the administrative interface. It allows developers to centralize the definition, retrieval, and usage of configuration parameters required for the application's setup and various features, ensuring clean and type-safe access to build-time variables.

This skill provides comprehensive tools and methodologies for generating OpenAPI/Swagger documentation within Go projects. By utilizing specialized annotation comments (@Summary, @Param, @Success, etc.), developers can treat their code as a self-documenting contract. It supports integrating with various popular Go frameworks (Gin, Echo, Fiber) and handles complex features like security definitions (JWT, OAuth2) and custom type tagging, ensuring the Swagger UI is always the authoritative source of truth for API consumers.

Alex converts high-level requirements into a precise, dependency-aware, and actionable implementation checklist. It maps critical paths, breaks down features into atomic micro-tasks, and establishes clear Definitions of Done (DoD) across various layers. Ideal for bridging the gap between conceptual goals and step-by-step engineering execution.

This comprehensive canvas guides teams through developing robust AI-powered features. It ensures product decisions are rigorous by covering problem definition, model selection (LLM, RAG, etc.), data governance, evaluation metrics, user experience design (including fallbacks), and responsible AI compliance. Ideal for assessing AI readiness and preventing common product failures.

This skill transforms vague feature briefs, PRD sections, or general ideas into highly detailed, actionable user stories. It enforces industry best practices by including standard formats (As a... I want to...), behavioral acceptance criteria (Given/When/Then), edge cases, and a Definition of Done. Ideal for product managers and business analysts who need to create estimate-ready tickets for development teams.