tdd-guide

The tdd-guide is an advanced engineering skill designed to integrate Test Driven Development (TDD) seamlessly into agentic development workflows. It acts as a specialized pair-programmer for engineering teams, focusing on code reliability, maintainability, and architectural integrity. The skill is built for developers and engineering subagents who need consistent, high-quality test suites across diverse projects, supporting major frameworks including Jest, Pytest, JUnit, Vitest, Mocha, and RSpec.

• Intelligent test generation that transforms user stories, API specifications, and business requirements into executable, high-quality test cases.

• Automated coverage analysis that parses LCOV, JSON, and XML reports to identify untested paths, error handlers, and branch logic gaps.

• Step-by-step guidance through the Red-Green-Refactor cycle, including validation checks to ensure tests correctly map to requirements.

• Advanced metrics tracking including cyclomatic complexity, cognitive complexity, and testability scoring to prevent technical debt.

• Framework-specific boilerplate creation, including proper import management, mock data generation, fixture creation, and setup/teardown logic.

• Context-aware recommendations prioritized by severity (P0-P2), helping developers focus on critical safety gaps before nice-to-have refactoring.

• To use the skill effectively, provide source code or file paths along with specific requirements or coverage reports to receive tailored test suites.

• It supports multiple input methods including direct file paths, raw code snippets, or structured coverage data from CI/CD pipelines.

• Users can trigger the skill by referencing @tdd-guide, followed by a command such as analyzing coverage or requesting a test case generation for a specific module.

• The output is optimized for the user's environment, providing rich markdown for Claude Desktop or concise, terminal-friendly JSON output for CI/CD automation.

• The skill includes a suite of utilities for identifying test smells, analyzing flakiness, detecting slow tests, and performing boundary value analysis.

• It is best utilized in environments requiring high reliability, such as security-sensitive modules, complex API logic, or large-scale legacy codebase refactoring.