validation

Feature Validation Skill

Purpose

This skill provides systematic validation of implemented features, ensuring code quality, test coverage, performance, security, and requirement fulfillment before marking work complete.

When to Use

• After implementation and testing are complete
• Before creating pull request
• Before marking feature as done
• When verifying all acceptance criteria met
• Final quality gate before deployment

Validation Workflow

1. Code Quality Validation

Run Quality Checks:

# Format check (Black)
black --check src/ tests/

# Type checking (mypy)
mypy src/

# Linting (flake8, if configured)
flake8 src/ tests/

# All checks together
make lint  # If Makefile configured

Quality Checklist: Refer to quality-checklist.md for comprehensive review

Key Quality Metrics:

• All functions have type hints
• All public functions have docstrings (Google style)
• No files exceed 500 lines
• No lint errors or warnings
• Code formatted with Black
• Type checking passes with mypy
• No code duplication (DRY principle)
• Single responsibility principle followed

Automated Script:

# Use validation script
python scripts/run_checks.py --quality

Deliverable: Quality report with pass/fail

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2. Test Coverage Validation

Run Tests with Coverage:

# Run all tests with coverage
pytest --cov=src --cov-report=html --cov-report=term-missing

# Check coverage threshold
pytest --cov=src --cov-fail-under=80

# View HTML coverage report
open htmlcov/index.html

Coverage Checklist:

• Overall coverage ≥ 80%
• Core business logic ≥ 90%
• Utilities and helpers ≥ 85%
• No critical paths untested
• All branches covered
• Edge cases tested
• Error conditions tested

Identify Coverage Gaps:

# Show untested lines
pytest --cov=src --cov-report=term-missing

# Generate detailed HTML report
pytest --cov=src --cov-report=html

Deliverable: Coverage report with gaps identified