MCP HubMCP Hub
返回技能列表

go-testing

alex-ilgayev
更新于 Yesterday
39 次查看
485
70
485
在 GitHub 上查看
测试aitestingmcpdesign

关于

The go-testing skill handles Golang testing tasks including running tests, writing new tests, and debugging failures specifically for the MCPSpy project. It follows project conventions like using `require` for critical assertions and `assert` for non-critical ones. Use this skill when working with Go test files, implementing test cases, or fixing test issues in the codebase.

快速安装

Claude Code

推荐
插件命令推荐
/plugin add https://github.com/alex-ilgayev/MCPSpy
Git 克隆备选方式
git clone https://github.com/alex-ilgayev/MCPSpy.git ~/.claude/skills/go-testing

在 Claude Code 中复制并粘贴此命令以安装该技能

技能文档

Go Testing Skill

Provides guidance and automation for Golang testing tasks in the MCPSpy project.

Testing Philosophy

  • Use require library for assertions that should stop test execution on failure
  • Use assert library for non-critical assertions where test should continue
  • Choose internal vs external package testing based on what needs to be tested
  • Test internal functions by placing test files in the same package (no _test suffix)
  • Avoid creating externally facing functions solely for testing purposes

When to Use This Skill

  • Running unit tests with go test
  • Writing new test files and test cases
  • Debugging and fixing failing tests
  • Implementing test fixtures and mocks
  • Improving test coverage for the MCPSpy project

GitHub 仓库

alex-ilgayev/MCPSpy
路径: .claude/skills/go-testing
aiai-securityllmmcpmonitoringsecurity

相关推荐技能

content-collections

Content Collections 是一个 TypeScript 优先的构建工具,可将本地 Markdown/MDX 文件转换为类型安全的数据集合。它专为构建博客、文档站和内容密集型 Vite+React 应用而设计,提供基于 Zod 的自动模式验证。该工具涵盖从 Vite 插件配置、MDX 编译到生产环境部署的完整工作流。

查看技能

creating-opencode-plugins

该Skill为开发者创建OpenCode插件提供指导,涵盖命令、文件、LSP等25+种事件类型。它详细说明了插件结构、事件API规范及JavaScript/TypeScript实现模式,帮助开发者构建事件驱动的模块。适用于需要拦截操作、扩展功能或自定义AI助手行为的插件开发场景。

查看技能

sglang

SGLang是一个专为LLM设计的高性能推理框架,特别适用于需要结构化输出的场景。它通过RadixAttention前缀缓存技术,在处理JSON、正则表达式、工具调用等具有重复前缀的复杂工作流时,能实现极速生成。如果你正在构建智能体或多轮对话系统,并追求远超vLLM的推理性能,SGLang是理想选择。

查看技能

evaluating-llms-harness

测试

该Skill通过60+个学术基准测试(如MMLU、GSM8K等)评估大语言模型质量,适用于模型对比、学术研究及训练进度追踪。它支持HuggingFace、vLLM和API接口,被EleutherAI等行业领先机构广泛采用。开发者可通过简单命令行快速对模型进行多任务批量评估。

查看技能