claude-skills-reference/engineering-team/tdd-guide

TDD Guide - Test Driven Development Skill

Version: 1.0.0 Last Updated: November 5, 2025 Author: Claude Skills Factory

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

Table of Contents

• Overview
• Features
• Installation
• Quick Start
• Python Modules
• Usage Examples
• Configuration
• Supported Frameworks
• Output Formats
• Best Practices
• Troubleshooting
• Contributing
• License

Overview

The TDD Guide skill transforms how engineering teams implement Test Driven Development by providing:

• Intelligent Test Generation: Convert requirements into executable test cases
• Coverage Analysis: Parse LCOV, JSON, XML reports and identify gaps
• Multi-Framework Support: Jest, Pytest, JUnit, Vitest, and more
• TDD Workflow Guidance: Step-by-step red-green-refactor guidance
• Quality Metrics: Comprehensive test and code quality analysis
• Context-Aware Output: Optimized for Desktop, CLI, or API usage

Features

Test Generation (3 capabilities)

1. Generate Test Cases from Requirements - User stories → Test cases
2. Create Test Stubs - Proper scaffolding with framework patterns
3. Generate Test Fixtures - Realistic test data and boundary values

TDD Workflow (3 capabilities)

1. Red-Green-Refactor Guidance - Phase-by-phase validation
2. Suggest Missing Scenarios - Identify untested edge cases
3. Review Test Quality - Isolation, assertions, naming analysis

Coverage & Metrics (6 categories)

1. Test Coverage - Line/branch/function with gap analysis
2. Code Complexity - Cyclomatic/cognitive complexity
3. Test Quality - Assertions, isolation, naming scoring
4. Test Data - Boundary values, edge cases
5. Test Execution - Timing, slow tests, flakiness
6. Missing Tests - Uncovered paths and error handlers

Framework Integration (4 capabilities)

1. Multi-Framework Adapters - Jest, Pytest, JUnit, Vitest, Mocha
2. Generate Boilerplate - Proper imports and test structure
3. Configure Runners - Setup and coverage configuration
4. Framework Detection - Automatic framework identification

Installation

Claude Code (Desktop)

1. Download the skill folder:

   # Option A: Clone from repository
   git clone https://github.com/your-org/tdd-guide-skill.git
   
   # Option B: Download ZIP and extract
   

2. Install to Claude skills directory:

   # Project-level (recommended for team projects)
   cp -r tdd-guide /path/to/your/project/.claude/skills/
   
   # User-level (available for all projects)
   cp -r tdd-guide ~/.claude/skills/
   

3. Verify installation:

   ls ~/.claude/skills/tdd-guide/
   # Should show: SKILL.md, *.py files, samples
   

Claude Apps (Browser)

1. Use the skill-creator skill to import the ZIP file
2. Or manually upload files through the skills interface

Claude API

# Upload skill via API
import anthropic

client = anthropic.Anthropic(api_key="your-api-key")

# Create skill with files
skill = client.skills.create(
    name="tdd-guide",
    files=["tdd-guide/SKILL.md", "tdd-guide/*.py"]
)

Quick Start

1. Generate Tests from Requirements

@tdd-guide

Generate tests for password validation function:
- Min 8 characters
- At least 1 uppercase, 1 lowercase, 1 number, 1 special char

Language: TypeScript
Framework: Jest

2. Analyze Coverage

@tdd-guide

Analyze coverage from: coverage/lcov.info
Target: 80% coverage
Prioritize recommendations

3. TDD Workflow

@tdd-guide

Guide me through TDD for implementing user authentication.

Requirements: Email/password login, session management
Framework: Pytest

Python Modules

The skill includes 8 Python modules organized by functionality:

Core Modules (7 files)

1. test_generator.py (450 lines)

   • Generate test cases from requirements
   • Create test stubs with proper structure
   • Suggest missing scenarios based on code analysis
   • Support for multiple test types (unit, integration, e2e)

2. coverage_analyzer.py (380 lines)

   • Parse LCOV, JSON, XML coverage reports
   • Calculate line/branch/function coverage
   • Identify coverage gaps with prioritization
   • Generate actionable recommendations

3. metrics_calculator.py (420 lines)

   • Cyclomatic and cognitive complexity analysis
   • Test quality scoring (isolation, assertions, naming)
   • Test smell detection
   • Execution metrics analysis

4. framework_adapter.py (480 lines)

   • Multi-framework adapters (Jest, Pytest, JUnit, Vitest, Mocha)
   • Generate framework-specific imports and structure
   • Assertion syntax translation
   • Setup/teardown hook generation

5. tdd_workflow.py (380 lines)

   • Red-Green-Refactor phase guidance
   • Phase validation and progression
   • Refactoring suggestions
   • Workflow state management

6. fixture_generator.py (340 lines)

   • Boundary value generation
   • Edge case scenario creation
   • Mock data generation from schemas
   • Fixture file export (JSON, YAML, Python)

7. format_detector.py (280 lines)

   • Automatic language detection
   • Testing framework identification
   • Coverage format detection
   • Project structure analysis

Utility Modules (1 file)

8. output_formatter.py (260 lines)
   • Context-aware formatting (Desktop, CLI, API)
   • Progressive disclosure
   • Token-efficient reporting
   • Summary and detailed views

Total Lines of Code

• Python: ~3,400 lines
• Documentation: ~1,200 lines
• Samples: ~200 lines
• Total: ~4,400 lines

Usage Examples

Example 1: Generate Tests from User Story

Input:

@tdd-guide

User Story: As a user, I want to add items to my shopping cart

Acceptance Criteria:
- Add single item with quantity
- Update quantity if item already exists
- Validate quantity is positive
- Return updated cart total

Language: Python
Framework: Pytest

Output:

import pytest
from shopping_cart import ShoppingCart

class TestShoppingCart:
    """Test suite for shopping cart functionality."""

    def test_add_single_item_successfully(self):
        """Should add single item with quantity."""
        cart = ShoppingCart()
        cart.add_item('apple', quantity=3, price=1.50)

        assert len(cart.items) == 1
        assert cart.items['apple']['quantity'] == 3
        assert cart.get_total() == 4.50

    def test_update_quantity_for_existing_item(self):
        """Should update quantity if item already exists."""
        cart = ShoppingCart()
        cart.add_item('apple', quantity=3, price=1.50)
        cart.add_item('apple', quantity=2, price=1.50)

        assert cart.items['apple']['quantity'] == 5
        assert cart.get_total() == 7.50

    def test_reject_negative_quantity(self):
        """Should validate quantity is positive."""
        cart = ShoppingCart()

        with pytest.raises(ValueError, match="Quantity must be positive"):
            cart.add_item('apple', quantity=-1, price=1.50)

Example 2: Coverage Analysis with Recommendations

Input:

@tdd-guide

Analyze coverage:

[Coverage report data]
SF:src/auth/login.ts
DA:1,5
DA:2,5
DA:3,3
DA:4,0
DA:5,0
...

Target: 80% coverage

Output:

## Coverage Summary

- Line Coverage: 65.0%
- Branch Coverage: 58.3%
- Function Coverage: 75.0%

⚠️ **65.0%** coverage (yellow) - Below 80% threshold

## Recommendations

### 🔴 Critical (P0)

1. **Error handling paths not covered**
   - Action: Add tests for authentication failure scenarios
   - File: `src/auth/login.ts`
   - Lines: 45-52, 67-70

2. **Branch coverage gap in validation**
   - Action: Test all conditional branches in input validation
   - File: `src/auth/login.ts`
   - Lines: 23-28

### 🟡 Important (P1)

1. **Edge cases missing**
   - Action: Add boundary tests for password length (7, 8, 9 chars)
   - Impact: Medium

Example 3: TDD Red-Green-Refactor Guidance

Input:

@tdd-guide

Guide TDD for email validation.

Requirement: Email must contain @ and valid domain

Phase: RED (write failing test)

Output:

## TDD Phase: RED

### Goal
Write a failing test that describes desired email validation behavior

### Steps
1. ✅ Read requirement: Email must contain @ and valid domain
2. ✅ Think about expected behavior
3. ⏳ Write test that verifies this behavior
4. ⏳ Run test and ensure it fails
5. ⏳ Verify failure reason is correct (not syntax error)

### Test Template (Jest)

```typescript
describe('EmailValidator', () => {
  it('should accept valid email format', () => {
    // Arrange
    const validator = new EmailValidator();

    // Act
    const result = validator.validate('user@example.com');

    // Assert
    expect(result).toBe(true);
  });

  it('should reject email without @ symbol', () => {
    const validator = new EmailValidator();
    const result = validator.validate('userexample.com');
    expect(result).toBe(false);
  });
});

Next Steps

1. Run the test (it should fail)
2. Verify failure: "EmailValidator is not defined" or similar
3. Share test result to proceed to GREEN phase

### Example 4: Test Quality Review

**Input**:

@tdd-guide

Review test quality:

def test_user_login(): result = login("admin", "password123") assert result assert result["status"] == "success" assert result["token"] assert result["user"]["id"] > 0 assert result["user"]["role"] == "admin"

**Output**:
```markdown
## Test Quality Analysis

**Overall Score**: 62/100

### Issues Detected

1. **Assertion Roulette** (High Severity)
   - 5 assertions in single test
   - **Fix**: Split into multiple focused tests

2. **Poor Test Isolation** (Medium Severity)
   - Test depends on database state ("admin" user existing)
   - **Fix**: Use fixtures or mocks for user data

3. **Weak Assertions** (Medium Severity)
   - `assert result["token"]` just checks truthy (could be any string)
   - **Fix**: `assert len(result["token"]) > 20` or use regex

4. **Missing Naming Context** (Low Severity)
   - Test name doesn't describe what's being tested
   - **Fix**: `test_successful_login_returns_valid_token_and_user_data`

### Recommended Refactoring

```python
@pytest.fixture
def admin_user():
    return {"username": "admin", "password": "password123"}

def test_successful_login_returns_success_status(admin_user):
    """Test that valid credentials return success status."""
    result = login(admin_user["username"], admin_user["password"])
    assert result["status"] == "success"

def test_successful_login_returns_valid_jwt_token(admin_user):
    """Test that successful login returns a JWT token."""
    result = login(admin_user["username"], admin_user["password"])
    assert len(result["token"]) > 20
    assert result["token"].count('.') == 2  # JWT format

def test_successful_login_returns_user_data(admin_user):
    """Test that successful login includes user information."""
    result = login(admin_user["username"], admin_user["password"])
    assert result["user"]["id"] > 0
    assert result["user"]["role"] == "admin"

Quality Improvements

• Isolation Score: 55 → 90 (with fixtures)
• Naming Quality: 40 → 85 (descriptive names)
• Test Focus: 30 → 95 (one assertion per test)

## Configuration

### Environment Variables

```bash
# Set preferred testing framework
export TDD_DEFAULT_FRAMEWORK="jest"

# Set coverage threshold
export TDD_COVERAGE_THRESHOLD=80

# Set output verbosity
export TDD_VERBOSE=true

# Set output format
export TDD_OUTPUT_FORMAT="markdown"  # or "json", "terminal"

Skill Configuration (Optional)

Create .tdd-guide.json in project root:

{
  "framework": "jest",
  "language": "typescript",
  "coverage_threshold": 80,
  "test_directory": "tests/",
  "quality_rules": {
    "max_assertions_per_test": 3,
    "require_descriptive_names": true,
    "enforce_isolation": true
  },
  "output": {
    "format": "markdown",
    "verbose": false,
    "max_recommendations": 10
  }
}

Supported Frameworks

JavaScript/TypeScript

• Jest 29+ (recommended for React, Node.js)
• Vitest 0.34+ (recommended for Vite projects)
• Mocha 10+ with Chai
• Jasmine 4+

Python

• Pytest 7+ (recommended)
• unittest (Python standard library)
• nose2 0.12+

Java

• JUnit 5 5.9+ (recommended)
• TestNG 7+
• Mockito 5+ (mocking support)

Coverage Tools

• Istanbul/nyc (JavaScript)
• c8 (JavaScript, V8 native)
• coverage.py (Python)
• pytest-cov (Python)
• JaCoCo (Java)
• Cobertura (multi-language)

Output Formats

Markdown (Claude Desktop)

• Rich formatting with headers, tables, code blocks
• Visual indicators (✅, ⚠️, ❌)
• Progressive disclosure (summary first, details on demand)
• Syntax highlighting for code examples

Terminal (Claude Code CLI)

• Concise, text-based output
• Clear section separators
• Minimal formatting for readability
• Quick scanning for key information

JSON (API/CI Integration)

• Structured data for automated processing
• Machine-readable metrics
• Suitable for CI/CD pipelines
• Easy integration with other tools

Best Practices

Test Generation

1. Start with requirements - Clear specs lead to better tests
2. Cover the happy path first - Then add error and edge cases
3. One behavior per test - Focused tests are easier to maintain
4. Use descriptive names - Tests are documentation

Coverage Analysis

1. Aim for 80%+ coverage - Balance between safety and effort
2. Prioritize critical paths - Not all code needs 100% coverage
3. Branch coverage matters - Line coverage alone is insufficient
4. Track trends - Coverage should improve over time

TDD Workflow

1. Small iterations - Write one test, make it pass, refactor
2. Run tests frequently - Fast feedback loop is essential
3. Commit often - Each green phase is a safe checkpoint
4. Refactor with confidence - Tests are your safety net

Test Quality

1. Isolate tests - No shared state between tests
2. Fast execution - Unit tests should be <100ms each
3. Deterministic - Same input always produces same output
4. Clear failures - Good error messages save debugging time

Troubleshooting

Common Issues

Issue: Generated tests have wrong syntax for my framework

Solution: Explicitly specify framework
Example: "Generate tests using Pytest" or "Framework: Jest"

Issue: Coverage report not recognized

Solution: Verify format (LCOV, JSON, XML)
Try: Paste raw coverage data instead of file path
Check: File exists and is readable

Issue: Too many recommendations, overwhelmed

Solution: Ask for prioritized output
Example: "Show only P0 (critical) recommendations"
Limit: "Top 5 recommendations only"

Issue: Test quality score seems wrong

Check: Ensure complete test context (setup/teardown included)
Verify: Test file contains actual test code, not just stubs
Context: Quality depends on isolation, assertions, naming

Issue: Framework detection incorrect

Solution: Specify framework explicitly
Example: "Using JUnit 5" or "Framework: Vitest"
Check: Ensure imports are present in code

File Structure

tdd-guide/
├── SKILL.md                          # Skill definition (YAML + documentation)
├── README.md                         # This file
├── HOW_TO_USE.md                     # Usage examples
│
├── test_generator.py                 # Test generation core
├── coverage_analyzer.py              # Coverage parsing and analysis
├── metrics_calculator.py             # Quality metrics calculation
├── framework_adapter.py              # Multi-framework support
├── tdd_workflow.py                   # Red-green-refactor guidance
├── fixture_generator.py              # Test data and fixtures
├── format_detector.py                # Automatic format detection
├── output_formatter.py               # Context-aware output
│
├── sample_input_typescript.json      # TypeScript example
├── sample_input_python.json          # Python example
├── sample_coverage_report.lcov       # LCOV coverage example
└── expected_output.json              # Expected output structure

Contributing

We welcome contributions! To contribute:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/improvement)
3. Make your changes
4. Add tests for new functionality
5. Run validation: python -m pytest tests/
6. Commit changes (git commit -m "Add: feature description")
7. Push to branch (git push origin feature/improvement)
8. Open a Pull Request

Development Setup

# Clone repository
git clone https://github.com/your-org/tdd-guide-skill.git
cd tdd-guide-skill

# Install development dependencies
pip install -r requirements-dev.txt

# Run tests
pytest tests/ -v

# Run linter
pylint *.py

# Run type checker
mypy *.py

Version History

v1.0.0 (November 5, 2025)

• Initial release
• Support for TypeScript, JavaScript, Python, Java
• Jest, Pytest, JUnit, Vitest framework adapters
• LCOV, JSON, XML coverage parsing
• TDD workflow guidance (red-green-refactor)
• Test quality metrics and analysis
• Context-aware output formatting
• Comprehensive documentation

License

MIT License - See LICENSE file for details

Support

• Documentation: See HOW_TO_USE.md for detailed examples
• Issues: Report bugs via GitHub issues
• Questions: Ask in Claude Code community forum
• Updates: Check repository for latest version

Acknowledgments

Built with Claude Skills Factory toolkit, following Test Driven Development best practices and informed by:

• Kent Beck's "Test Driven Development: By Example"
• Martin Fowler's refactoring catalog
• xUnit Test Patterns by Gerard Meszaros
• Growing Object-Oriented Software, Guided by Tests

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Ready to improve your testing workflow? Install the TDD Guide skill and start generating high-quality tests today!