Domain-Driven Development workflow specialist using ANALYZE-PRESERVE-IMPROVE cycle for behavior-preserving code transformation. Use when refactoring legacy code, improving code structure without functional changes, reducing technical debt, or performing API migration with behavior preservation. Do NOT use for writing new tests (use moai-workflow-testing instead) or creating new features from scratch (use expert-backend or expert-frontend instead).
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
Domain-Driven Development workflow specialist using ANALYZE-PRESERVE-IMPROVE cycle for behavior-preserving code transformation. Use when refactoring legacy code, improving code structure without functional changes, reducing technical debt, or performing API migration with behavior preservation. Do NOT use for writing new tests (use moai-workflow-testing instead) or creating new features from scratch (use expert-backend or expert-frontend instead).
[NOTE] This workflow is selected based on .moai/config/sections/quality.yaml:
constitution:development_mode:hybrid# or ddd, tddhybrid_settings:new_features:tdd# New code → use TDDlegacy_refactoring:ddd# Existing code → use DDD (this workflow)
When to use this workflow:
development_mode: ddd → Always use DDD
development_mode: hybrid + refactoring existing code → Use DDD
development_mode: hybrid + new package/module → Use TDD instead (moai-workflow-tdd)
Key distinction:
New file/package (doesn't exist yet) → TDD (RED-GREEN-REFACTOR)
Domain-Driven Development provides a systematic approach for refactoring existing codebases where behavior preservation is paramount. Unlike TDD which creates new functionality, DDD improves structure without changing behavior.
PRESERVE: Characterization tests, behavior snapshots, test safety net verification
IMPROVE: Incremental structural changes with continuous behavior validation
When to Use DDD:
Refactoring legacy code with existing tests
Improving code structure without functional changes
Technical debt reduction in production systems
API migration and deprecation handling
Code modernization projects
When DDD is not applicable because code already exists
Greenfield projects (with adapted cycle - see below)
When NOT to Use DDD:
When behavior changes are required (modify SPEC first)
Greenfield Project Adaptation:
For new projects without existing code, DDD adapts its cycle:
ANALYZE: Requirements analysis instead of code analysis
PRESERVE: Define intended behavior through specification tests (test-first)
IMPROVE: Implement code to satisfy the defined tests
This makes DDD a superset of TDD - it includes TDD's test-first approach while also supporting refactoring scenarios.
Core Philosophy
DDD vs TDD Comparison
TDD Approach (for new features):
Cycle: RED-GREEN-REFACTOR
Goal: Create new functionality through tests
Starting Point: No code exists
Test Type: Specification tests that define expected behavior
Outcome: New working code with test coverage
DDD Approach (for refactoring):
Cycle: ANALYZE-PRESERVE-IMPROVE
Goal: Improve structure without behavior change
Starting Point: Existing code with defined behavior
Test Type: Characterization tests that capture current behavior
Outcome: Better structured code with identical behavior
Behavior Preservation Principle
The golden rule of DDD is that observable behavior must remain identical before and after refactoring. This means:
All existing tests must pass unchanged
API contracts remain identical
Side effects remain identical
Performance characteristics remain within acceptable bounds
Implementation Guide
Phase 1: ANALYZE
The analyze phase focuses on understanding the current codebase structure and identifying refactoring opportunities.
Domain Boundary Identification
Identify logical boundaries in the codebase by examining:
Module dependencies and import patterns
Data flow between components
Shared state and coupling points
Public API surfaces
Use AST-grep to analyze structural patterns. For Python, search for import patterns to understand module dependencies. For class hierarchies, analyze inheritance relationships and method distributions.
Coupling and Cohesion Metrics
Evaluate code quality metrics:
Afferent Coupling (Ca): Number of classes depending on this module
Efferent Coupling (Ce): Number of classes this module depends on
Instability (I): Ce / (Ca + Ce) - higher means less stable
Abstractness (A): Abstract classes / Total classes
Distance from Main Sequence: |A + I - 1|
Low cohesion and high coupling indicate refactoring candidates.
Structural Analysis Patterns
Use AST-grep to identify problematic patterns:
God classes with too many methods or responsibilities
Feature envy where methods use other class data excessively
Long parameter lists indicating missing abstractions
Duplicate code patterns across modules
Create analysis reports documenting:
Current architecture overview
Identified problem areas with severity ratings
Proposed refactoring targets with risk assessment
Dependency graphs showing coupling relationships
Phase 2: PRESERVE
The preserve phase establishes safety nets before making any changes.
Characterization Tests
Characterization tests capture existing behavior without assumptions about correctness. The goal is to document what the code actually does, not what it should do.
Steps for creating characterization tests:
Step 1: Identify critical code paths through execution
Step 2: Create tests that exercise these paths
Step 3: Let tests fail initially to discover actual output
Step 4: Update tests to expect actual output
Step 5: Document any surprising behavior discovered
Characterization test naming convention: testcharacterize[component]_[scenario]
Behavior Snapshots
For complex outputs, use snapshot testing to capture current behavior:
API response snapshots
Serialization output snapshots
State transformation snapshots
Error message snapshots
Snapshot files serve as behavior contracts during refactoring.
Test Safety Net Verification
Before proceeding to improvement phase, verify:
All existing tests pass (100% green)
New characterization tests cover refactoring targets
Code coverage meets threshold for affected areas
No flaky tests exist in the safety net
Run mutation testing if available to verify test effectiveness.
Phase 3: IMPROVE
The improve phase makes structural changes while continuously validating behavior preservation.
Incremental Transformation Strategy
Never make large changes at once. Follow this pattern:
Make smallest possible structural change
Run full test suite
If tests fail, revert immediately
If tests pass, commit the change
Repeat until refactoring goal achieved
Safe Refactoring Patterns
Extract Method: When a code block can be named and isolated. Use AST-grep to identify candidates by searching for repeated code blocks or long methods.
Extract Class: When a class has multiple responsibilities. Move related methods and fields to a new class while maintaining the original API through delegation.
Move Method: When a method uses data from another class more than its own. Relocate while preserving all call sites.
Inline Refactoring: When indirection adds complexity without benefit. Replace delegation with direct implementation.
Rename Refactoring: When names do not reflect current understanding. Update all references atomically using AST-grep rewrite.
AST-Grep Assisted Transformations
Use AST-grep for safe, semantic-aware transformations:
For method extraction, create a rule that identifies the code pattern and rewrites to the extracted form.
For API migration, create a rule that matches old API calls and rewrites to new API format.
For deprecation handling, create rules that identify deprecated patterns and suggest modern alternatives.
Continuous Validation Loop
After each transformation:
Run unit tests (fast feedback)
Run integration tests (behavior validation)
Run characterization tests (snapshot comparison)
Verify no new warnings or errors introduced
Check performance benchmarks if applicable
DDD Workflow Execution
Standard DDD Session
When executing DDD through moai:2-run in DDD mode:
Step 1 - Initial Assessment:
Read SPEC document for refactoring scope
Identify affected files and components
Assess current test coverage
Step 2 - Analyze Phase Execution:
Run AST-grep analysis on target code
Generate coupling and cohesion metrics
Create domain boundary map
Document refactoring opportunities
Step 3 - Preserve Phase Execution:
Verify all existing tests pass
Create characterization tests for uncovered paths
Generate behavior snapshots
Confirm safety net adequacy
Step 4 - Improve Phase Execution:
Execute transformations incrementally
Run tests after each change
Commit successful changes immediately
Document any discovered issues
Step 5 - Validation and Completion:
Run full test suite
Compare before/after metrics
Verify all behavior snapshots match
Generate refactoring report
DDD Loop Pattern
For complex refactoring requiring multiple iterations:
Set maximum loop iterations based on scope
Each loop focuses on one refactoring target
Exit conditions: all targets adddessed or iteration limit reached
Progress tracking through TODO list updates
Quality Metrics
DDD Success Criteria
Behavior Preservation (Required):
All pre-existing tests pass
All characterization tests pass
No API contract changes
Performance within bounds
Structure Improvement (Goals):
Reduced coupling metrics
Improved cohesion scores
Reduced code complexity
Better separation of concerns
DDD-Specific TRUST Validation
Apply TRUST 5 framework with DDD focus:
Testability: Characterization test coverage adequate
Readability: Naming and structure improvements verified
Understandability: Domain boundaries clearer
Security: No new vulnerabilities introduced
Transparency: All changes documented and traceable
Integration Points
With AST-Grep Skill
DDD relies heavily on AST-grep for:
Structural code analysis
Pattern identification
Safe code transformations
Multi-file refactoring
Load moai-tool-ast-grep for detailed pattern syntax and rule creation.
With Testing Workflow
DDD complements testing workflow:
Uses characterization tests from testing patterns
Integrates with mutation testing for safety net validation
Leverages snapshot testing infrastructure
With Quality Framework
DDD outputs feed into quality assessment:
Before/after metrics comparison
TRUST 5 validation for changes
Technical debt tracking
Troubleshooting
Common Issues
Tests Fail After Transformation:
Revert immediately to last known good state
Analyze which tests failed and why
Check if transformation changed behavior unintentionally
Consider smaller transformation steps
Characterization Tests Are Flaky:
Identify sources of non-determinism
Mock external dependencies
Fix time-dependent or order-dependent behavior
Consider snapshot tolerance settings
Performance Degradation:
Profile before and after
Identify hot paths affected by changes
Consider caching or optimization
Document acceptable trade-offs
Recovery Procedures
When DDD session encounters issues:
Save current state with git stash
Reset to last successful commit
Review transformation that caused failure
Plan alternative approach
Resume from preserved state
Version: 1.0.0
Status: Active
Last Updated: 2026-01-16
