Skill UI

This comprehensive guide covers structured concurrency patterns in Go, focusing on goroutines, channels, mutexes, and various sync primitives. It provides principles for writing, reviewing, and auditing concurrent code to ensure leak-freedom, correct ownership management, and robust error handling, covering topics like context propagation and race condition avoidance.

Improves Go error handling by treating errors as structured data. This utility adds critical context—such as domain, user IDs, stack traces, and machine-readable error codes—to standard Go errors. This capability is vital for enhancing observability, enabling APM tools (like Sentry or Datadog) and on-call engineers to diagnose complex issues quickly without relying on manual log correlation.

Comprehensive reference for red teamers and penetration testers on executing Active Directory attacks, covering reconnaissance, credential harvesting, Kerberos abuses, lateral movement, and domain persistence techniques against Windows domains.

This skill teaches users to apply the scientific method to software debugging. Instead of random changes, it guides the process through five steps: Observation, Hypothesizing, Prediction, Testing, and Analysis. By systematically forming and testing specific, measurable hypotheses, users can efficiently isolate failing components and confirm the true root cause of technical issues.

Synchronizes mature lessons from a domain-specific memory file into the matching instruction file inside VS Code, letting you choose global or workspace scope so you can preserve knowledge, cut redundancy, and finalize updates in a controlled merge process.

A comprehensive, multi-phase quality engineering audit tool designed to test codebases beyond standard unit tests. It first explores the codebase's domain and architecture to derive behavioral requirements, then executes a rigorous sequence including spec-traced functional tests, three-pass code reviews, and a multi-model specification audit (Council of Three). It is specifically engineered to find the critical 35% of real-world defects that structural code review alone might miss. Works with any programming language.

This guide serves as a comprehensive reference for source-file migration patterns required when upgrading to React 19. It details critical API changes, such as updating `ReactDOM.render` to `createRoot().render()`, modernizing ref handling, and adjusting legacy context usage. It is essential for developers migrating large, existing React applications to ensure compatibility with the latest React standards.

Guides React 19 test migrations by outlining act import changes, Simulate-to-fireEvent replacements, full react-dom/test-utils cleanup, and StrictMode call-count verification strategies.

A comprehensive automation toolkit designed to manage the entire Vercel deployment lifecycle and associated infrastructure. Users can programmatically monitor, trigger, and manage deployments, configure custom domains and DNS records, manage environment variables across multiple targets, and oversee project settings and project listings. Essential for implementing robust CI/CD pipelines and managing cloud infrastructure.

A comprehensive guide providing best practices for Git workflows, covering adaptation strategies for major Chinese platforms (Gitee, Coding.net, etc.), branch naming conventions, standardized commit messaging (Chinese Conventional Commits), and CI/CD integration across various domestic environments. Helps streamline development processes for Chinese teams.

This skill guides the parallel assignment of specialized agents when debugging multiple, independent failure domains. Instead of using one agent for all issues, isolate each problem (e.g., a specific test file or subsystem) to its own dedicated agent. This allows agents to work concurrently without sharing context or interfering with each other, significantly accelerating the debugging process and maintaining context purity.

The Hugging Face Transformers library is a comprehensive toolkit providing access to thousands of pre-trained models across diverse domains including Natural Language Processing (NLP), Computer Vision, Audio, and Multimodal tasks. It enables developers to easily load models, perform high-level inference (e.g., text generation, classification, QA), and fine-tune models on custom datasets using streamlined APIs like Pipelines and the Trainer class. Ideal for building advanced, state-of-the-art AI applications.