Skill UI

This skill provides comprehensive guidelines for writing idiomatic, robust, and production-ready error handling in Go. It covers best practices for error creation, proper wrapping using `%w`, inspection techniques with `errors.Is` and `errors.As`, adhering to the single handling rule, and utilizing structured logging with `slog`. Use it when auditing, reviewing, or writing new Go code to ensure reliable error propagation and logging.

A comprehensive guide to Go (Golang) naming conventions. It covers best practices for packages, structs, interfaces, constants, errors, and more. Use this skill when writing, reviewing, or refactoring Go code to ensure idiomatic and clear naming that follows Go standards (e.g., MixedCaps, constructor rules, error handling).

A library providing type-safe monadic abstractions for Go, including Option, Result, and Either. It allows developers to eliminate nil pointer risks and manage complex error handling using composable, functional pipelines, significantly improving code robustness and readability in Go applications.

samber/oops is a robust library that transforms standard Go error handling by adding crucial structured context. Instead of generic error messages, it allows developers to attach key-value attributes, stack traces, error codes, and user-facing messages directly to the error object. This is essential for modern microservices, allowing APM tools (Datadog, Sentry) and on-call engineers to diagnose failures accurately by understanding the full context of the failure, such as the domain, user ID, and specific query that failed.

A type-safe, declarative implementation of ReactiveX in Go. This library enables robust handling of infinite, asynchronous event streams (like WebSockets or tickers) by providing composable operators, automatic backpressure management, and error propagation. It is designed to replace complex, error-prone manual channel and goroutine wiring, allowing developers to build predictable, real-time, event-driven pipelines.

This guide details best practices for using `lark-cli` to interact with Feishu resources. It covers initial configuration, switching between user and bot identities, and crucial authentication flows for AI agents. It provides detailed instructions on handling permission errors, implementing secure split-flow authentication, and managing high-risk write operations to ensure data integrity and user consent.

OpenViking Code Repository Search exposes `ov` terminal commands for scoped semantic, keyword, and glob searches across viking:// resources, lets you read directory summaries/files, and manage indexed repositories (add/remove) with documented error handling.

This generator provides a comprehensive, production-ready template for building a Model Context Protocol (MCP) server using Kotlin. It sets up the full Gradle structure, integrates essential dependencies (MCP SDK, Ktor, Coroutines), and includes robust placeholders for custom tools, error handling, and testing, accelerating the development of context-aware backend services.

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.

Produces production-ready Dataverse SDK code for Python, showcasing advanced error handling and retries, batch operations, OData query tuning, metadata lifecycle management, configuration/timeouts, cache invalidation, chunked file uploads, and Pandas integrations with references.

Master the art of developing robust and scalable Python applications that interface with Microsoft Dataverse. This skill covers implementing enterprise-grade best practices, including comprehensive error handling using DataverseError hierarchy, advanced retry logic with exponential backoff, and optimizing API calls using OData standards (filtering, selecting, and expanding). It ensures code is clean, maintainable, and production-ready.

A comprehensive guide for creating, updating, refactoring, and reviewing solutions built on the Microsoft Agent Framework. This skill provides unified best practices for both .NET and Python environments, serving as a modern successor to Semantic Kernel and AutoGen. It emphasizes adherence to the latest official documentation, async patterns, robust error handling, and structured agent/workflow orchestration for complex AI applications.