Skill UI

Provides comprehensive migration patterns for converting existing v5 Webiny code to the v6 architecture. It details key architectural shifts, including replacing ContextPlugins with Dependency Injection (DI) Services, updating event subscriptions to specialized EventHandler Features, and adopting the feature-based container registration model. Essential for developers and AI agents performing major framework upgrades.

A deep-dive session designed to stress-test a technical plan against an existing domain model. This process forces the user to resolve ambiguities, sharpen terminology, and validate decisions by cross-referencing code and existing documentation (CONTEXT.md, ADRs). It ensures architectural rigor and completeness before implementation.

This guide assists developers in building robust Model Context Protocol (MCP) servers and clients using C#/.NET. It specifically targets the stable 1.x NuGet packages, providing detailed instructions for configuring various transports (STDIO, Streamable HTTP), defining custom tools, prompts, and resources. It helps solve common integration issues like stale preview pinning, complex HTTP wiring, and structured content handling, ensuring production-ready, standard-compliant code.

A comprehensive guide for developing modular applications and microservices using the tinystruct Java framework. It details essential patterns for handling request routing (CLI/HTTP dual-mode), managing application context, defining actions with annotations, and performing native JSON serialization using the Builder component.

This command initiates a structured founder interview to capture critical, durable facts about the company. It covers basics, business models, financials, and strategic priorities across 12 key areas. The output, `company-context.md`, serves as the single source of truth for all AI agents and advisors, ensuring all decision-making is based on accurate, comprehensive company context rather than assumptions.

Expert skill for building Model Context Protocol (MCP) servers and tools from scratch. Covers the full development lifecycle—from defining tool schemas and resources to implementation, testing, and publishing to registries. Supports robust patterns using TypeScript and Python for integrating custom capabilities into AI agents.