Skill UI

This advanced tool surfaces critical, often unrecorded, evidence of human-AI collaboration during development sessions. It analyzes git diffs and conversational context across multiple layers to classify the signal level (HIGH/MEDIUM/LOW), measure technical complexity, detect decision forks, and quantify the specific contribution of the AI model, providing structured data for post-session retrospectives and accountability.

This guide serves as a comprehensive reference for writing idiomatic, efficient, and modern Python code. It covers best practices for comprehensions, context managers, error handling (EAFP), data class usage, type hinting, and avoiding common anti-patterns, helping developers write cleaner, more robust, and truly 'Pythonic' code.

This skill guides the decomposition of monolithic applications into independent microservices using Domain-Driven Design (DDD). It defines bounded contexts, service boundaries, communication patterns (sync/async), data ownership, and provides a structured migration roadmap (e.g., Strangler Fig Pattern). Essential for scaling and modernizing large enterprise systems.

This document provides a comprehensive guide to the principles and vocabulary needed to write high-quality, predictable, and effective skills. It covers advanced concepts like model-invoked vs. user-invoked skills, managing context load, structuring information hierarchies (steps vs. reference), and implementing progressive disclosure to improve agent predictability.

This skill audits Model Context Protocol (MCP) servers to detect critical AI security vulnerabilities. It scans for tool poisoning, tool shadowing, rug pulls, SSRF, and unauthenticated exposure. Use it before integrating new tools or during security reviews to ensure agent reliability and prevent supply chain attacks.

This skill provides comprehensive best practices for designing modern and robust React user interfaces. It covers critical patterns for managing asynchronous data, including optimal loading state indicators (skeletons vs. spinners), structured error handling hierarchies, and designing contextual empty states. Essential for improving user experience during data fetching and operations.

This skill helps developers achieve maximum AI readiness by automatically detecting the project stack and seeding context using a single, precise goal sentence. It systematically fills the essential project context (the 6 Ws), requiring the user only to answer the few remaining knowledge gaps (like the 'why' or 'when'). This creates a portable, structured context file, ensuring AI models have a complete understanding of the project before coding, minimizing guesswork and maximizing efficiency.

This guide provides a comprehensive reference on Agent Skills, a standardized and powerful method for providing structured context, workflows, and tools to Large Language Models (LLMs). It details the anatomy of the SKILL.md file, the three-stage progressive disclosure loading process, and best practices for creating both capability (tool-based) and process (workflow-based) primitives. Use this resource when designing, reviewing, or debugging agent-driven applications to ensure reliable and context-efficient performance.

Scaffolds a DDD bounded context with the standard domain, application and infrastructure layout, generating barrel index files and registering the context in the domain model graph. Ideal when starting a new subdomain or splitting a monolith.

A comprehensive guide detailing modern architectural patterns for building high-performance user interfaces with React and Next.js. Covers advanced techniques including Compound Components, Render Props, custom state hooks (like useQuery and useDebounce), advanced state management (Context + Reducer), and performance optimization strategies like memoization and code splitting.

Reference patterns and best practices for building Model Context Protocol (MCP) servers, covering tool, resource and prompt definitions, transport choices, capability negotiation, and integration with AI clients.

A comprehensive guide to building robust, maintainable, and idiomatic Perl applications using modern features (Perl 5.36+). This resource covers best practices for object-oriented programming (Moo/Moose), advanced error handling (Try::Tiny), context sensitivity, complex regex usage, and efficient module design.