Night Market Skill — ported from claude-night-market/sanctum. For the full experience with agents, hooks, and commands, install the Claude Code plugin.
Table of Contents
Test Updates and Maintenance
Overview
detailed test management system that applies TDD/BDD principles to maintain, generate, and enhance tests across codebases. This skill practices what it preaches - it uses TDD principles for its own development and serves as a living example of best practices.
Core Philosophy
- RED-GREEN-REFACTOR: Strict adherence to TDD cycle
- Behavior-First: BDD patterns that describe what code should do
- Meta Dogfooding: The skill's own tests demonstrate the principles it teaches
- Quality Gates: detailed validation before considering tests complete
What It Is
A modular test management system that:
- Discovers what needs testing or updating
- Generates tests following TDD principles
- Enhances existing tests with BDD patterns
- Validate test quality through multiple lenses
Quick Start
Quick Checklist for First Time Use
detailed Test Update
# Run full test update workflow
Skill(test-updates)
Verification: Run pytest -v to verify tests pass.
Targeted Test Updates
# Update tests for specific paths
Skill(test-updates) --target src/sanctum/agents
Skill(test-updates) --target tests/test_commit_messages.py
Verification: Run pytest -v to verify tests pass.
TDD for New Features
# Apply TDD to new code
Skill(test-updates) --tdd-only --target new_feature.py
Verification: Run pytest -v to verify tests pass.
Using the Scripts Directly
Human-Readable Output:
# Analyze test coverage gaps
python plugins/sanctum/scripts/test_analyzer.py --scan src/
# Generate test scaffolding
python plugins/sanctum/scripts/test_generator.py \
--source src/my_module.py --style pytest_bdd
# Check test quality
python plugins/sanctum/scripts/quality_checker.py \
--validate tests/test_my_module.py
Verification: Run pytest -v to verify tests pass.
Programmatic Output (for Claude Code):
# Get JSON output for programmatic parsing - test_analyzer
python plugins/sanctum/scripts/test_analyzer.py \
--scan src/ --output-json
# Returns:
# {
# "success": true,
# "data": {
# "source_files": ["src/module.py", ...],
# "test_files": ["tests/test_module.py", ...],
# "uncovered_files": ["module_without_tests", ...],
# "coverage_gaps": [{"file": "...", "reason": "..."}]
# }
# }
# Get JSON output - test_generator
python plugins/sanctum/scripts/test_generator.py \
--source src/my_module.py --output-json
# Returns:
# {
# "success": true,
# "data": {
# "test_file": "path/to/test_my_module.py",
# "source_file": "src/my_module.py",
# "style": "pytest_bdd",
# "fixtures_included": true,
# "edge_cases_included": true,
# "error_cases_included": true
# }
# }
# Get JSON output - quality_checker
python plugins/sanctum/scripts/quality_checker.py \
--validate tests/test_my_module.py --output-json
# Returns:
# {
# "success": true,
# "data": {
# "static_analysis": {...},
# "dynamic_validation": {...},
# "metrics": {...},
# "quality_score": 85,
# "quality_level": "QualityLevel.GOOD",
# "recommendations": [...]
# }
# }
Verification: Run pytest -v to verify tests pass.
When To Use It
Use this skill when you need to:
- Update tests after code changes
- Generate tests for new features
- Improve existing test quality
- validate detailed test coverage
Perfect for:
- Pre-commit test validation
- CI/CD pipeline integration
- Refactoring with test safety
- Onboarding new developers
When NOT To Use
- Auditing
test suites - use pensive:test-review
- Writing production code
- focus on implementation first
- Auditing
test suites - use pensive:test-review
- Writing production code
- focus on implementation first
Workflow Integration
Phase 1: Discovery
- Scan codebase for test gaps
- Analyze recent changes
- Identify broken or outdated tests
See modules/test-discovery.md for detection patterns.
Phase 2: Strategy
- Choose appropriate BDD style (see
modules/bdd-patterns.md)
- Plan test structure
- Define quality criteria
Phase 3: Implementation
- Write failing tests (RED) - see
modules/tdd-workflow.md
- Implement minimal passing code (GREEN)
- Refactor for clarity (REFACTOR)
See modules/test-generation.md for generation templates.
Phase 4: Validation
- Static analysis and linting
- Dynamic test execution
- Coverage and quality metrics
See modules/quality-validation.md for validation criteria.
Quality Assurance
The skill applies multiple quality checks:
- Static: Linting, type checking, pattern validation
- Dynamic: Test execution in sandboxed environments
- Metrics: Coverage, mutation score, complexity analysis
- Review: Structured checklists for peer validation
Examples
BDD-Style Test Generation
See modules/bdd-patterns.md for additional patterns.
class TestGitWorkflow:
"""BDD-style tests for Git workflow operations."""
def test_commit_workflow_with_staged_changes(self):
"""
GIVEN a Git repository with staged changes
WHEN the user runs the commit workflow
THEN it should create a commit with proper message format
AND all tests should pass
"""
# Test implementation following TDD principles
pass
Verification: Run pytest -v to verify tests pass.
Test Enhancement
- Add edge cases and error scenarios
- Include performance benchmarks
- Add mutation testing for robustness
See modules/test-enhancement.md for enhancement strategies.
Integration with Existing Skills
- git-workspace-review: Get context of changes
- file-analysis: Understand code structure
- test-driven-development: Apply strict TDD discipline
- skills-eval: Validate quality and compliance
Success Metrics
- Test coverage > 85%
- All tests follow BDD patterns
- Zero broken tests in CI
- Mutation score > 80%
Troubleshooting FAQ
Common Issues
Q: Tests are failing after generation
A: This is expected! The skill follows TDD principles - generated tests are designed to fail first. Follow the RED-GREEN-REFACTOR cycle:
- Run the test and confirm it fails for the right reason
- Implement minimal code to make it pass
- Refactor for clarity
Q: Quality score is low despite having tests
A: Check for these common issues:
- Missing BDD patterns (Given/When/Then)
- Vague assertions like
assert result is not None
- Tests without documentation
- Long, complex tests (>50 lines)
Q: Generated tests don't match my code structure
A: The scripts analyze AST patterns and may need guidance:
- Use
--style flag to match your preferred BDD style
- Check that source files have proper function/class definitions
- Review the generated scaffolding and customize as needed
Q: Mutation testing takes too long
A: Mutation testing is resource-intensive:
- Use
--quick-mutation flag for subset testing
- Focus on critical modules first
- Run overnight for detailed analysis
Q: Can't find tests for my file
A: The analyzer uses naming conventions:
- Source:
my_module.py → Test: test_my_module.py
- Check that test files follow pytest naming patterns
- validate test directory structure is standard
Performance Tips
- Large codebases: Use
--target to focus on specific directories
- CI integration: Run validation in parallel with other checks
- Memory usage: Process files in batches for very large projects
Getting Help
- Check script outputs for detailed error messages
- Use
--verbose flag for more information
- Review the validation report for specific recommendations
- Start with small modules to understand patterns before scaling
