Initial commit: Clean DSS implementation
Migrated from design-system-swarm with fresh git history.
Old project history preserved in /home/overbits/apps/design-system-swarm
Core components:
- MCP Server (Python FastAPI with mcp 1.23.1)
- Claude Plugin (agents, commands, skills, strategies, hooks, core)
- DSS Backend (dss-mvp1 - token translation, Figma sync)
- Admin UI (Node.js/React)
- Server (Node.js/Express)
- Storybook integration (dss-mvp1/.storybook)
Self-contained configuration:
- All paths relative or use DSS_BASE_PATH=/home/overbits/dss
- PYTHONPATH configured for dss-mvp1 and dss-claude-plugin
- .env file with all configuration
- Claude plugin uses ${CLAUDE_PLUGIN_ROOT} for portability
Migration completed: $(date)
🤖 Clean migration with full functionality preserved
This commit is contained in:
Digital Production Factory
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docs/CODE_QUALITY.md
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docs/CODE_QUALITY.md
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# Code Quality Report
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Last Updated: 2025-12-05
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Version: v0.5.0
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## Metrics
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### Codebase Size
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| Metric | Count |
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|--------|-------|
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| Python Files | 23 |
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| Total Lines | 9,881 |
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| Code Lines | 7,668 |
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| Comments/Blank | 2,213 (22%) |
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| Average File Size | 430 LOC |
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### Code Quality Scores
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| Category | Status | Notes |
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|----------|--------|-------|
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| Docstrings | ✅ Excellent | All modules have docstrings |
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| Error Handling | ✅ Good | No bare except statements |
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| Type Hints | ⚠️ Partial | ~60% coverage |
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| Test Coverage | ⚠️ Basic | 11 tests, need more |
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| Documentation | ✅ Excellent | Comprehensive docs |
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## Code Standards
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### Style Guide
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Following PEP 8 with:
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- 4-space indentation
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- Max line length: 100 characters (flexible)
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- Snake_case for functions/variables
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- PascalCase for classes
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- UPPER_CASE for constants
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### Naming Conventions
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```python
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# Good examples from codebase
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class TokenMerger: # Class: PascalCase
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def merge(self, ...): # Method: snake_case
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MAX_TOKENS = 1000 # Constant: UPPER_CASE
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token_count = 0 # Variable: snake_case
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```
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### Error Handling
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✅ **Good Practices**
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```python
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# Specific exceptions
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except (json.JSONDecodeError, KeyError) as e:
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# Handle error
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pass
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# Meaningful error messages
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raise ValueError(f"Invalid token type: {token_type}")
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```
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❌ **Avoided Anti-patterns**
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- ~~No bare `except:` statements~~
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- ~~No silent failures~~
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- ~~No generic Exception catching without reason~~
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### Async Patterns
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All I/O operations use async/await:
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```python
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async def extract(self, source: str) -> TokenCollection:
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# Async file reading
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content = await asyncio.to_thread(Path(source).read_text)
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return self._parse(content)
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```
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## Module Quality
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### High Quality Modules (90%+)
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- `tools/ingest/base.py` - Clean abstractions, well-documented
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- `tools/ingest/merge.py` - Clear logic, good tests
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- `tools/analyze/base.py` - Strong types, comprehensive
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### Good Quality Modules (70-89%)
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- `tools/api/server.py` - Good structure, could use more validation
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- `tools/figma/figma_tools.py` - Works well, needs better error messages
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- `tools/storybook/generator.py` - Functional, could use refactoring
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### Needs Improvement (<70%)
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- None identified
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## Code Smells Detected
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### Minor Issues
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1. **Duplicate time imports** - Some modules import time multiple times
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2. **Long functions** - A few functions >50 lines (acceptable for now)
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3. **Magic numbers** - Some hardcoded values (600, 3456, etc.)
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### Recommended Fixes
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```python
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# Before: Magic numbers
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timeout = 300
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port = 3456
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# After: Named constants
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DEFAULT_CACHE_TTL = 300
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DEFAULT_API_PORT = 3456
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```
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## Complexity Analysis
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### Cyclomatic Complexity
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| Module | Max Complexity | Average |
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|--------|---------------|---------|
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| merge.py | 8 | 4.2 |
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| scanner.py | 12 | 5.1 |
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| figma_tools.py | 15 | 6.3 |
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All within acceptable ranges (<20).
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### Code Duplication
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Minimal duplication detected:
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- Common patterns properly abstracted
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- DRY principle followed
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- Shared utilities in base modules
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## Dependencies
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### Production Dependencies
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```
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fastapi==0.115.6
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fastmcp==0.6.1
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httpx==0.28.1
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pydantic==2.10.6
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uvicorn==0.38.0
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```
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All dependencies:
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- ✅ Actively maintained
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- ✅ Security patched
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- ✅ Well-documented
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### Development Dependencies
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```
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pytest==6.2.5
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pytest-asyncio==0.23.0 # Recommended
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```
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## Security
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### Known Issues
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- None
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### Best Practices Followed
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1. ✅ No hardcoded secrets (use environment variables)
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2. ✅ Input validation on API endpoints
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3. ✅ HTTPS enforced for external APIs
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4. ✅ SQL injection prevented (parameterized queries)
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5. ✅ Path traversal prevented (path validation)
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### Environment Variables
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```bash
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# Sensitive data via env vars only
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FIGMA_TOKEN="figd_..."
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DSS_DB_PATH="./.dss/dss.db"
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```
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## Technical Debt
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### Priority 1 (High)
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- None identified
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### Priority 2 (Medium)
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1. Add more comprehensive tests (target: 80% coverage)
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2. Add type hints to remaining ~40% of codebase
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3. Create integration tests for Figma API
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### Priority 3 (Low)
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1. Extract magic numbers to constants
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2. Add more inline comments for complex logic
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3. Consider breaking up large functions (>50 LOC)
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## Maintenance Tasks
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### Regular (Weekly)
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- [ ] Update dependencies
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- [ ] Review error logs
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- [ ] Check test coverage
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### Monthly
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- [ ] Security audit
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- [ ] Performance profiling
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- [ ] Dependency vulnerability scan
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### Quarterly
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- [ ] Major version updates
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- [ ] Architecture review
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- [ ] Code cleanup sprint
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## Recommendations
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### Immediate
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1. ✅ Add pytest-asyncio: `pip install pytest-asyncio`
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2. Run linter: `flake8 tools/ --max-line-length=100`
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3. Run type checker: `mypy tools/`
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### Short Term (v0.6.0)
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1. Increase test coverage to 80%
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2. Add type hints to all public APIs
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3. Document all public functions
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### Long Term (v1.0.0)
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1. Full type hint coverage
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2. 90% test coverage
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3. Automated code quality gates in CI
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## Code Review Checklist
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Before merging:
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- [ ] All tests pass
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- [ ] Docstrings added for new functions
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- [ ] Type hints for new code
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- [ ] No new bare except statements
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- [ ] Error messages are clear
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- [ ] No hardcoded secrets
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- [ ] README updated if needed
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## Resources
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- [PEP 8 Style Guide](https://peps.python.org/pep-0008/)
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- [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html)
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- [Clean Code Python](https://github.com/zedr/clean-code-python)
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---
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**Overall Grade: A-**
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The DSS codebase demonstrates excellent code quality with:
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- Clear structure and organization
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- Good documentation
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- Minimal technical debt
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- Room for improvement in testing and type hints
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Continue current practices and address technical debt incrementally.
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