Skill UI

This skill enforces a rigorous, 5-phase debugging protocol (Reproduce, Isolate, Root Cause, Fix, Verify) for AI coding agents. It forces a disciplined approach, preventing agents from guessing fixes or making premature edits. Use this when debugging complex, intermittent, or hard-to-isolate bugs to ensure the root cause is fully confirmed before any code changes are applied, maximizing debugging accuracy.

This resource is designed to document and guide the implementation of various architectural patterns and best practices when developing backend applications using the NestJS framework. It helps developers structure their code for maximum scalability and maintainability.

This tool provides command-line capabilities for subscribing to and consuming real-time events from the Lark/Feishu platform. It streams various events (e.g., IM messages, reactions, member changes) as NDJSON, making it ideal for developing Lark bots, implementing real-time message processing pipelines, or powering long-running AI agents. Users can list available event keys, inspect schemas, and consume data with advanced filtering, timeout, and max-event controls.

A comprehensive workflow for the full lifecycle management of AI skills. This tool guides users through creating skills from scratch, modifying existing ones, and rigorously benchmarking performance. It supports iterative refinement, quantitative evaluation, and optimizing skill descriptions for maximum triggering accuracy.

Guides multi-axis reviews before merging, checking correctness, readability, architecture, security, and performance while enforcing concise change sizing and descriptive commit messaging to keep PR history clear.

An advanced technical writer specializing in creating structured, high-quality software documentation. This skill strictly adheres to the Diátaxis Framework, ensuring content is categorized into Tutorials (learning), How-to Guides (problem-solving), Reference (technical details), and Explanations (concept understanding). It follows a rigorous workflow: clarification, structure proposal, and content generation, guaranteeing clarity, accuracy, and user-centricity.

This guide outlines comprehensive best practices for documenting Java code using Javadoc comments. It covers how to properly document public, protected, package-private, and private members, along with the correct usage of various tags like @param, @return, @throws, @see, and specialized syntax for code snippets and versioning. Following these standards ensures API documentation is accurate, maintainable, and adheres to professional Java development norms.

This is an expert guide for writing, debugging, and validating complex expressions within n8n workflows. It covers core variables such as $json, $node, $now, and $env, detailing how to correctly access nested data from current nodes, previous nodes, and webhook payloads. Learn best practices for handling data structures, managing variable scopes, and avoiding common syntax errors to ensure reliable workflow automation.

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 skill guides the integration of Arize AX tracing into existing applications, especially complex LLM applications. It follows a structured, two-phase, agent-assisted flow: first analyzing the codebase (read-only) to understand dependencies, and then implementing instrumentation. It supports multi-language environments (Python, TypeScript, Java, Go) and integrates deeply with OpenTelemetry and various LLM providers, ensuring comprehensive observability without altering core business logic.

Conducts a multistep preflight validation for Azure Bicep deployments, covering syntax checks, what-if previews, permission verification, and report generation so teams can confidently run azd or az deployments with known outcomes.

A comprehensive guide covering best practices for writing effective unit tests using the TUnit framework for .NET. Topics include structuring tests using the Arrange-Act-Assert (AAA) pattern, implementing data-driven tests via attributes, utilizing fluent assertion syntax, managing complex test lifecycles (Class/Assembly level hooks), and migrating concepts from xUnit to TUnit. Learn how to write robust, isolated, and advanced tests.