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tdd-guideTDD 指南

Agent Skill

tdd-guide 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

791

周安装

32

GitHub Stars

732

下载量

248
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:tdd-guide(TDD 指南)
来源仓库:https://github.com/alirezarezvani/claude-code-skill-factory
仓库路径:skills/tdd-guide
安装命令:
npx skills add https://github.com/alirezarezvani/claude-code-skill-factory --skill tdd-guide
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/alirezarezvani/claude-code-skill-factory --skill tdd-guide

简介

提供测试驱动开发(TDD)的智能引导与自动化支持。

  • 支持从需求生成测试用例与框架集成建议。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。
  • 覆盖 Jest、Pytest、Cypress 等多语言测试生态。
  • 通过 GitHub 安装,建议结合项目技术栈选择适配模板。
  • tdd-guide 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

TDD Guide - Test Driven Development for Engineering Teams

A comprehensive Test Driven Development skill that provides intelligent test generation, coverage analysis, framework integration, and TDD workflow guidance across multiple languages and testing frameworks.

Capabilities

Test Generation

  • Generate Test Cases from Requirements: Convert user stories, API specs, and business requirements into executable test cases
  • Create Test Stubs: Generate test function scaffolding with proper naming, imports, and setup/teardown
  • Generate Test Fixtures: Create realistic test data, mocks, and fixtures for various scenarios

TDD Workflow Support

  • Guide Red-Green-Refactor: Step-by-step guidance through TDD cycles with validation
  • Suggest Missing Scenarios: Identify untested edge cases, error conditions, and boundary scenarios
  • Review Test Quality: Analyze test isolation, assertions quality, naming conventions, and maintainability

Coverage & Metrics Analysis

  • Calculate Coverage: Parse LCOV, JSON, and XML coverage reports for line/branch/function coverage
  • Identify Untested Paths: Find code paths, branches, and error handlers without test coverage
  • Recommend Improvements: Prioritized recommendations (P0/P1/P2) for coverage gaps and test quality

Framework Integration

  • Multi-Framework Support: Jest, Pytest, JUnit, Vitest, Mocha, RSpec adapters
  • Generate Boilerplate: Create test files with proper imports, describe blocks, and best practices
  • Configure Test Runners: Set up test configuration, coverage tools, and CI integration

Comprehensive Metrics

  • Test Coverage: Line, branch, function coverage with gap analysis
  • Code Complexity: Cyclomatic complexity, cognitive complexity, testability scoring
  • Test Quality: Assertions per test, isolation score, naming quality, test smell detection
  • Test Data: Boundary value analysis, edge case identification, mock data generation
  • Test Execution: Timing analysis, slow test detection, flakiness detection
  • Missing Tests: Uncovered edge cases, error handling gaps, missing integration scenarios

Input Requirements

The skill supports automatic format detection for flexible input:

Source Code

  • Languages: TypeScript, JavaScript, Python, Java
  • Format: Direct file paths or copy-pasted code blocks
  • Detection: Automatic language/framework detection from syntax and imports

Test Artifacts

  • Coverage Reports: LCOV (.lcov), JSON (coverage-final.json), XML (cobertura.xml)
  • Test Results: JUnit XML, Jest JSON, Pytest JSON, TAP format
  • Format: File paths or raw coverage data

Requirements (Optional)

  • User Stories: Text descriptions of functionality
  • API Specifications: OpenAPI/Swagger, REST endpoints, GraphQL schemas
  • Business Requirements: Acceptance criteria, business rules

Input Methods

  • Option A: Provide file paths (skill will read files)
  • Option B: Copy-paste code/data directly
  • Option C: Mix of both (automatically detected)

Output Formats

The skill provides context-aware output optimized for your environment:

Code Files

  • Test Files: Generated tests (Jest/Pytest/JUnit/Vitest) with proper structure
  • Fixtures: Test data files, mock objects, factory functions
  • Mocks: Mock implementations, stub functions, test doubles

Reports

  • Markdown: Rich coverage reports, recommendations, quality analysis (Claude Desktop)
  • JSON: Machine-readable metrics, structured data for CI/CD integration
  • Terminal-Friendly: Simplified output for Claude Code CLI

Smart Defaults

  • Desktop/Apps: Rich markdown with tables, code blocks, visual hierarchy
  • CLI: Concise, terminal-friendly format with clear sections
  • CI/CD: JSON output for automated processing

Progressive Disclosure

  • Summary First: High-level overview (<200 tokens)
  • Details on Demand: Full analysis available (500-1000 tokens)
  • Prioritized: P0 (critical) → P1 (important) → P2 (nice-to-have)

How to Use

Basic Usage

@tdd-guide

I need tests for my authentication module. Here's the code:
[paste code or provide file path]

Generate comprehensive test cases covering happy path, error cases, and edge cases.

Coverage Analysis

@tdd-guide

Analyze test coverage for my TypeScript project. Coverage report: coverage/lcov.info

Identify gaps and provide prioritized recommendations.

TDD Workflow

@tdd-guide

Guide me through TDD for implementing a password validation function.

Requirements:
- Min 8 characters
- At least 1 uppercase, 1 lowercase, 1 number, 1 special char
- No common passwords

Multi-Framework Support

@tdd-guide

Convert these Jest tests to Pytest format:
[paste Jest tests]

Scripts

Core Modules

  • test_generator.py: Intelligent test case generation from requirements and code
  • coverage_analyzer.py: Parse and analyze coverage reports (LCOV, JSON, XML)
  • metrics_calculator.py: Calculate comprehensive test and code quality metrics
  • framework_adapter.py: Multi-framework adapter (Jest, Pytest, JUnit, Vitest)
  • tdd_workflow.py: Red-green-refactor workflow guidance and validation
  • fixture_generator.py: Generate realistic test data and fixtures
  • format_detector.py: Automatic language and framework detection

Utilities

  • complexity_analyzer.py: Cyclomatic and cognitive complexity analysis
  • test_quality_scorer.py: Test quality scoring (isolation, assertions, naming)
  • missing_test_detector.py: Identify untested paths and missing scenarios
  • output_formatter.py: Context-aware output formatting (Desktop vs CLI)

Best Practices

Test Generation

  1. Start with Requirements: Write tests from user stories before seeing implementation
  2. Test Behavior, Not Implementation: Focus on what code does, not how it does it
  3. One Assertion Focus: Each test should verify one specific behavior
  4. Descriptive Names: Test names should read like specifications

TDD Workflow

  1. Red: Write failing test first
  2. Green: Write minimal code to make it pass
  3. Refactor: Improve code while keeping tests green
  4. Repeat: Small iterations, frequent commits

Coverage Goals

  1. Aim for 80%+: Line coverage baseline for most projects
  2. 100% Critical Paths: Authentication, payments, data validation must be fully covered
  3. Branch Coverage Matters: Line coverage alone is insufficient
  4. Don't Game Metrics: Focus on meaningful tests, not coverage numbers

Test Quality

  1. Independent Tests: Each test should run in isolation
  2. Fast Execution: Keep unit tests under 100ms each
  3. Deterministic: Tests should always produce same results
  4. Clear Failures: Assertion messages should explain what went wrong

Framework Selection

  1. Jest: JavaScript/TypeScript projects (React, Node.js)
  2. Pytest: Python projects (Django, Flask, FastAPI)
  3. JUnit: Java projects (Spring, Android)
  4. Vitest: Modern Vite-based projects

Multi-Language Support

TypeScript/JavaScript

  • Frameworks: Jest, Vitest, Mocha, Jasmine
  • Runners: Node.js, Karma, Playwright
  • Coverage: Istanbul/nyc, c8

Python

  • Frameworks: Pytest, unittest, nose2
  • Runners: pytest, tox, nox
  • Coverage: coverage.py, pytest-cov

Java

  • Frameworks: JUnit 5, TestNG, Mockito
  • Runners: Maven Surefire, Gradle Test
  • Coverage: JaCoCo, Cobertura

Limitations

Scope

  • Unit Tests Focus: Primarily optimized for unit tests (integration tests require different patterns)
  • Static Analysis Only: Cannot execute tests or measure actual code behavior
  • Language Support: Best support for TypeScript, JavaScript, Python, Java (other languages limited)

Coverage Analysis

  • Report Dependency: Requires existing coverage reports (cannot generate coverage from scratch)
  • Format Support: LCOV, JSON, XML only (other formats need conversion)
  • Interpretation Context: Coverage numbers need human judgment for meaningfulness

Test Generation

  • Baseline Quality: Generated tests provide scaffolding, require human review and refinement
  • Complex Logic: Advanced business logic and integration scenarios need manual test design
  • Mocking Strategy: Mock/stub strategies should align with project patterns

Framework Integration

  • Configuration Required: Test runners need proper setup (this skill doesn't modify package.json or pom.xml)
  • Version Compatibility: Generated code targets recent stable versions (Jest 29+, Pytest 7+, JUnit 5+)

When NOT to Use This Skill

  • E2E Testing: Use dedicated E2E tools (Playwright, Cypress, Selenium)
  • Performance Testing: Use JMeter, k6, or Locust
  • Security Testing: Use OWASP ZAP, Burp Suite, or security-focused tools
  • Manual Testing: Some scenarios require human exploratory testing

Example Workflows

Workflow 1: Generate Tests from Requirements

Input: User story + API specification
Process: Parse requirements → Generate test cases → Create test stubs
Output: Complete test files ready for implementation

Workflow 2: Improve Coverage

Input: Coverage report + source code
Process: Identify gaps → Suggest tests → Generate test code
Output: Prioritized test cases for uncovered code

Workflow 3: TDD New Feature

Input: Feature requirements
Process: Guide red-green-refactor → Validate each step → Suggest refactorings
Output: Well-tested feature with clean code

Workflow 4: Framework Migration

Input: Tests in Framework A
Process: Parse tests → Translate patterns → Generate equivalent tests
Output: Tests in Framework B with same coverage

Integration Points

CI/CD Integration

  • Parse coverage reports from CI artifacts
  • Generate coverage badges and reports
  • Fail builds on coverage thresholds
  • Track coverage trends over time

IDE Integration

  • Generate tests for selected code
  • Run coverage analysis on save
  • Highlight untested code paths
  • Quick-fix suggestions for test gaps

Code Review

  • Validate test coverage in PRs
  • Check test quality standards
  • Identify missing test scenarios
  • Suggest improvements before merge

Version Support

  • Node.js: 16+ (Jest 29+, Vitest 0.34+)
  • Python: 3.8+ (Pytest 7+)
  • Java: 11+ (JUnit 5.9+)
  • TypeScript: 4.5+

Related Skills

This skill works well with:

  • code-review: Validate test quality during reviews
  • refactoring-assistant: Maintain tests during refactoring
  • ci-cd-helper: Integrate coverage in pipelines
  • documentation-generator: Generate test documentation

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Claude Code

30.05%
按下载量换算75

trae

21.14%
按下载量换算52

OpenCode

18.31%
按下载量换算45

Antigravity

12.47%
按下载量换算31

Gemini CLI

8.21%
按下载量换算20

replit

3.57%
按下载量换算9

安全审计

Gen Agent Trust Hub

可疑

Socket

通过

Snyk

通过

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。

来源信息

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