tdd-workflow
π―Skillfrom affaan-m/everything-claude-code
Enforces test-driven development by guiding developers to write comprehensive tests first, ensuring 80%+ code coverage across unit, integration, and E2E testing.
Overview
TDD Workflow is a skill that enforces test-driven development by guiding developers through a structured process of writing tests before code. It ensures 80%+ code coverage across unit, integration, and E2E tests with step-by-step TDD methodology.
Key Features
- Structured TDD workflow: user journeys, test cases, red-green-refactor cycle
- Coverage requirements of 80%+ including unit, integration, and E2E (Playwright) tests
- Testing patterns for Jest/Vitest, API testing, and Playwright browser automation
- Edge case, error scenario, and boundary condition coverage enforcement
- Step-by-step guidance from user story to verified coverage
Who is this for?
This skill is for developers and teams who want to adopt or enforce TDD practices with AI assistance. It is especially useful for projects where high test coverage is mandatory and developers need structured guidance on writing comprehensive test suites.
Same repository
affaan-m/everything-claude-code(43 items)
Installation
npx skills add https://github.com/affaan-m/everything-claude-code --skill tdd-workflowNeed more details? View full documentation on GitHub β
More from this repository10
Battle-tested Claude Code configurations from an Anthropic hackathon winner
Implements robust backend design patterns like repository, factory, singleton, and dependency injection for scalable and maintainable server-side architectures.
Validates and enforces consistent code quality, style guidelines, and best practices across programming languages and project structures.
Validates and secures code by providing comprehensive security checks for authentication, input handling, secrets management, and sensitive feature implementation.
Provides reusable React component patterns like composition, compound components, and render props to enhance code modularity and flexibility.
Provides reusable design patterns and idiomatic Go solutions for efficient, scalable, and maintainable software architecture.
Provides reusable SQL query patterns, database design strategies, and performance optimization techniques for PostgreSQL development
Dynamically updates and refines AI model knowledge through iterative feedback, adaptive learning techniques, and intelligent knowledge integration.
Streamlines Go testing with comprehensive unit, integration, and benchmark strategies, mocking frameworks, and test coverage analysis.
Dynamically adapts and improves AI performance through iterative feedback, knowledge expansion, and self-optimization techniques.