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DECISIONS.md
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DECISIONS.md
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# Architecture Decisions
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Documentation of key design decisions for Bubble Tea Designer skill.
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## Data Source Decision
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**Decision**: Use local charm-examples-inventory instead of API
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**Rationale**:
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- ✅ No rate limits or authentication needed
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- ✅ Fast lookups (local file system)
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- ✅ Complete control over inventory structure
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- ✅ Offline capability
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- ✅ Inventory can be updated independently
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**Alternatives Considered**:
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- GitHub API: Rate limits, requires authentication
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- Web scraping: Fragile, slow, unreliable
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- Embedded database: Adds complexity, harder to update
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**Trade-offs**:
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- User needs to have inventory locally (optional but recommended)
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- Updates require re-cloning repository
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## Analysis Approach
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**Decision**: 6 separate analysis functions + 1 comprehensive orchestrator
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**Rationale**:
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- ✅ Modularity - each function has single responsibility
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- ✅ Testability - easy to test individual components
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- ✅ Flexibility - users can call specific analyses
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- ✅ Composability - orchestrator combines as needed
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**Structure**:
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1. analyze_requirements() - NLP requirement extraction
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2. map_components() - Component scoring and selection
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3. select_patterns() - Example file matching
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4. design_architecture() - Structure generation
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5. generate_workflow() - Implementation planning
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6. comprehensive_tui_design_report() - All-in-one
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## Component Matching Algorithm
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**Decision**: Keyword-based scoring with manual taxonomy
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**Rationale**:
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- ✅ Transparent - users can see why components selected
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- ✅ Predictable - consistent results
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- ✅ Fast - O(n) search with indexing
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- ✅ Maintainable - easy to add new components
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**Alternatives Considered**:
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- ML-based matching: Overkill, requires training data
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- Fuzzy matching: Less accurate for technical terms
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- Rule-based expert system: Too rigid
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**Scoring System**:
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- Keyword match: 60 points max
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- Use case match: 40 points max
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- Total: 0-100 score per component
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## Architecture Generation Strategy
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**Decision**: Template-based with customization
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**Rationale**:
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- ✅ Generates working code immediately
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- ✅ Follows Bubble Tea best practices
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- ✅ Customizable per archetype
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- ✅ Educational - shows proper patterns
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**Templates Include**:
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- Model struct with components
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- Init() with proper initialization
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- Update() skeleton with message routing
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- View() with component rendering
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## Validation Strategy
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**Decision**: Multi-layer validation (requirements, components, architecture, workflow)
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**Rationale**:
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- ✅ Early error detection
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- ✅ Quality assurance
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- ✅ Helpful feedback to users
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- ✅ Catches incomplete designs
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**Validation Levels**:
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- CRITICAL: Must fix (empty description, no components)
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- WARNING: Should review (low coverage, many components)
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- INFO: Optional improvements
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## File Organization
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**Decision**: Modular scripts with shared utilities
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**Rationale**:
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- ✅ Clear separation of concerns
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- ✅ Reusable utilities
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- ✅ Easy to test
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- ✅ Maintainable codebase
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**Structure**:
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```
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scripts/
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main analysis scripts (6)
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utils/
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shared utilities
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validators/
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validation logic
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```
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## Pattern Matching Approach
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**Decision**: Inventory-based with ranking
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**Rationale**:
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- ✅ Leverages existing examples
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- ✅ Provides concrete references
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- ✅ Study order optimization
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- ✅ Realistic time estimates
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**Ranking Factors**:
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- Component usage overlap
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- Complexity match
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- Code quality/clarity
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## Documentation Strategy
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**Decision**: Comprehensive references with patterns and best practices
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**Rationale**:
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- ✅ Educational value
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- ✅ Self-contained skill
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- ✅ Reduces external documentation dependency
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- ✅ Examples for every pattern
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**References Created**:
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- Component guide (what each component does)
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- Design patterns (common architectures)
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- Best practices (dos and don'ts)
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- Example designs (complete real-world cases)
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## Performance Considerations
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**Optimizations**:
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- Inventory loaded once, cached in memory
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- Pre-computed component taxonomy
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- Fast keyword matching (no regex)
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- Minimal allocations in hot paths
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**Trade-offs**:
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- Memory usage: ~5MB for loaded inventory
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- Startup time: ~100ms for inventory loading
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- Analysis time: <1 second for complete report
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## Future Enhancements
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Potential improvements for v2.0:
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- Interactive mode for requirement refinement
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- Code generation (full implementation, not just scaffolding)
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- Live preview of designs
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- Integration with Go module initialization
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- Custom component definitions
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- Save/load design sessions
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