Nm Sanctum Test Updates

Updates, generates, and validates tests using git-workspace context and TDD/BDD methodology

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

• Overview
• Core Philosophy
• What It Is
• Quick Start
• Quick Checklist for First Time Use
• detailed Test Update
• Targeted Test Updates
• TDD for New Features
• Using the Scripts Directly
• When to Use It
• Workflow Integration
• Phase 1: Discovery
• Phase 2: Strategy
• Phase 3: Implementation
• Phase 4: Validation
• Quality Assurance
• Examples
• BDD-Style Test Generation
• Test Enhancement
• Integration with Existing Skills
• Success Metrics
• Troubleshooting FAQ
• Common Issues
• Performance Tips
• Getting Help

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
• Invariant-Encoding: Tests guard design decisions, not just behavior
• 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

• validate pytest is installed (pip install pytest)
• Have your source code in src/ or similar directory
• Create a tests/ directory if it doesn't exist
• Run Skill(sanctum:git-workspace-review) first to understand changes
• Start with Skill(test-updates) --target <specific-module> for focused updates

detailed Test Update

bash

# Run full test update workflow
Skill(test-updates)

Verification: Run pytest -v to verify tests pass.

Targeted Test Updates

bash

# 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

bash

# 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:

bash

# 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):

bash

# 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

1. Scan codebase for test gaps
2. Analyze recent changes
3. Identify broken or outdated tests

See modules/test-discovery.md for detection patterns.

Phase 2: Strategy

1. Choose appropriate BDD style (see modules/bdd-patterns.md)
2. Plan test structure
3. Define quality criteria
4. Identify design invariants to encode as tests

Phase 2.5: Invariant-Encoding Tests

Before writing behavioral tests, identify the design invariants that the code relies on and write tests that would break if those invariants were violated.

What to encode:

• Module boundary constraints (A never imports from B)
• Data flow direction (events flow publisher-to-subscriber, never the reverse)
• API contract shapes (public interfaces don't change without versioning)
• Data structure choices (if a map was chosen over a list, test the properties that justify that choice)
• Error handling strategies (fail-fast boundaries, recovery zones)

Example:

python

def test_plugins_never_import_from_other_plugins():
    """Encode the invariant: plugins are independent modules.

    If this test breaks, someone is coupling plugins
    directly. Present the 3 options to a human:
    1. Preserve: revert the import, keep plugins independent
    2. Layer: add a shared interface in leyline instead
    3. Revise: merge the plugins (requires ADR)
    """
    for plugin_dir in plugin_dirs:
        imports = extract_imports(plugin_dir)
        for imp in imports:
            assert not imp.startswith("plugins."), (
                f"{plugin_dir} imports {imp} — "
                f"violates plugin independence invariant"
            )

Why this matters: Tests that encode invariants are load-bearing. When an agent later encounters a feature that clashes with the invariant, the test failure forces a conscious decision rather than a silent drift. Without these tests, bad invariant decisions compound until the codebase is unsalvageable.

When updating existing tests:

If an invariant-encoding test needs to change, do NOT silently update the assertion. Flag it for human review with the three options: preserve the invariant, layer on top, or revise the invariant. This is a judgment call that requires human wisdom — models default to the "average" of training data and get these wrong far too often.

Phase 3: Implementation

1. Write failing tests (RED) - see modules/tdd-workflow.md
2. Implement minimal passing code (GREEN)
3. Refactor for clarity (REFACTOR)

See modules/test-generation.md for generation templates.

Phase 4: Validation

1. Static analysis and linting
2. Dynamic test execution
3. 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
• Invariant: Verify design-decision tests are not weakened
• Review: Structured checklists for peer validation

Examples

BDD-Style Test Generation

See modules/bdd-patterns.md for additional patterns.

python

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

1. git-workspace-review: Get context of changes
2. file-analysis: Understand code structure
3. test-driven-development: Apply strict TDD discipline
4. 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:

1. Run the test and confirm it fails for the right reason
2. Implement minimal code to make it pass
3. 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

1. Check script outputs for detailed error messages
2. Use --verbose flag for more information
3. Review the validation report for specific recommendations
4. Start with small modules to understand patterns before scaling