# Architectural Decisions

This document records the key architectural and design decisions made during the development of the Article-to-Prototype Skill.

---

## Decision 1: Simple Skill Architecture

**Context:** Need to choose between Simple Skill and Complex Skill Suite architecture.

**Decision:** Implemented as a Simple Skill with single focused objective.

**Rationale:**
- The skill has one clear purpose: article → prototype conversion
- Estimated ~1,800 lines of code fits Simple Skill criteria (<2,000 lines)
- All components work toward a single unified goal
- No need for multiple independent sub-skills
- Easier to maintain and understand

**Alternatives Considered:**
- **Skill Suite:** Would have separated extraction, analysis, and generation into independent skills
- **Rejected because:** Overhead of managing multiple skills, user would need to invoke separately, components are tightly coupled

---

## Decision 2: Multi-Format Extraction Strategy

**Context:** Users have articles in various formats (PDF, web, notebooks, markdown).

**Decision:** Implement specialized extractors for each format with a common interface.

**Rationale:**
- Each format has unique characteristics requiring specialized parsing
- Common `ExtractedContent` data structure allows downstream components to be format-agnostic
- Modular design enables easy addition of new formats
- Each extractor can use best-of-breed libraries (pdfplumber for PDF, trafilatura for web)

**Implementation:**
```python
# Common interface (duck typing)
class Extractor:
    def extract(self, source: str) -> ExtractedContent
```

**Alternatives Considered:**
- **Single Universal Extractor:** Would have limited effectiveness for specialized formats
- **Format Conversion Pipeline:** Would have converted everything to intermediate format; rejected due to information loss

---

## Decision 3: Language Selection Logic

**Context:** Need to automatically choose the best programming language for generated prototype.

**Decision:** Implemented priority-based selection with 4 levels.

**Selection Priority:**
1. Explicit user hint (highest priority)
2. Detected from code blocks in article
3. Domain-based best practices
4. Dependency-based inference
5. Default to Python (fallback)

**Rationale:**
- Respects user preference when given
- Leverages article's existing code examples
- Uses domain knowledge (ML → Python, Systems → Rust)
- Python is most versatile default

**Alternatives Considered:**
- **User Always Chooses:** Rejected because removes automation benefit
- **Fixed Language:** Rejected because limits usefulness
- **ML Model for Selection:** Rejected due to complexity and training requirements

---

## Decision 4: Prototype Generation Approach

**Context:** Generated code must be production-quality without placeholders.

**Decision:** Template-based generation with dynamic content insertion.

**Quality Requirements:**
- No TODO comments or placeholders
- Full error handling
- Type safety (hints/annotations)
- Comprehensive documentation
- Working test suite

**Rationale:**
- Templates ensure consistent structure
- Dynamic insertion allows customization
- Quality gates prevent incomplete output
- Users can immediately run and extend generated code

**Alternatives Considered:**
- **LLM-Based Generation:** Considered but requires API access and may produce inconsistent results
- **Code Snippets Only:** Rejected because users need complete, runnable projects
- **Interactive Wizard:** Rejected to maintain fully autonomous operation

---

## Decision 5: Modular Pipeline Architecture

**Context:** System has multiple distinct processing stages.

**Decision:** Implemented pipeline with independent, composable stages.

**Pipeline Stages:**
```
Input → Extraction → Analysis → Selection → Generation → Output
```

**Rationale:**
- Each stage has single responsibility
- Stages can be tested independently
- Easy to add new extractors, analyzers, or generators
- Clear data flow and error boundaries
- Supports caching at each stage

**Alternatives Considered:**
- **Monolithic Processor:** Rejected due to complexity and testing difficulty
- **Event-Driven Architecture:** Overengineered for current requirements

---

## Decision 6: Content Analysis Strategy

**Context:** Need to understand article content to make generation decisions.

**Decision:** Rule-based analysis with pattern matching and keyword scoring.

**Components:**
- Algorithm detection (regex patterns + structural analysis)
- Architecture recognition (keyword matching + context extraction)
- Domain classification (TF-IDF-like scoring)
- Dependency extraction (import statement parsing)

**Rationale:**
- Rule-based approach is deterministic and explainable
- No training data required
- Fast execution (<10 seconds)
- Easy to extend with new patterns
- Transparent to users

**Alternatives Considered:**
- **NLP/ML Models:** Rejected due to complexity, latency, and dependency overhead
- **LLM-Based Analysis:** Considered but requires API access and adds latency
- **Manual User Input:** Rejected to maintain full automation

---

## Decision 7: Dependency Management

**Context:** Generated projects need dependency manifests (requirements.txt, package.json, etc.).

**Decision:** Extract dependencies from analysis and supplement with domain defaults.

**Strategy:**
1. Extract from article imports/mentions
2. Add domain-specific defaults (ML → numpy, pandas)
3. Include only essential dependencies
4. Version pinning where detected

**Rationale:**
- Ensures generated code has required dependencies
- Domain defaults cover common cases
- Minimizes dependency bloat
- Users can easily modify manifest

**Alternatives Considered:**
- **All Possible Dependencies:** Rejected due to bloat and installation time
- **No Dependencies:** Rejected because code wouldn't run
- **Minimal Set Only:** Current approach balances completeness and minimalism

---

## Decision 8: Error Handling Strategy

**Context:** Many failure modes: network errors, corrupt PDFs, unsupported formats, etc.

**Decision:** Graceful degradation with informative error messages.

**Approach:**
- Try best strategy first, fall back to alternatives
- Partial extraction better than complete failure
- Detailed error messages with actionable suggestions
- Logging at multiple levels (INFO, DEBUG, ERROR)

**Example:**
```python
# Try pdfplumber, fallback to PyPDF2
if HAS_PDFPLUMBER:
    try:
        return self._extract_with_pdfplumber(pdf_path)
    except Exception as e:
        logger.warning(f"pdfplumber failed: {e}, trying PyPDF2")
        return self._extract_with_pypdf2(pdf_path)
```

**Rationale:**
- Maximizes success rate
- Provides useful feedback for failures
- Users can troubleshoot problems
- System degrades gracefully

---

## Decision 9: Testing Strategy

**Context:** Generated prototypes should include test scaffolding.

**Decision:** Generate basic test suite with placeholder tests and example integration test.

**Included Tests:**
- Integration test (main execution)
- Placeholder tests with instructive comments
- Test structure following language conventions

**Rationale:**
- Demonstrates testing approach
- Users can run tests immediately
- Encourages test-driven development
- Provides starting point for expansion

**What's NOT Included:**
- Complete test coverage (would be too opinionated)
- Mock data (users' data varies)
- Performance benchmarks (premature optimization)

---

## Decision 10: Caching Strategy

**Context:** Re-processing same article is wasteful.

**Decision:** Implemented multi-level cache with TTL.

**Cache Levels:**
1. Memory cache (current session)
2. Disk cache (24-hour TTL)
3. AgentDB (persistent learning)

**Rationale:**
- Improves performance for repeated operations
- Reduces API calls (web extraction)
- Enables offline re-processing
- 24-hour TTL balances freshness and performance

**Alternatives Considered:**
- **No Caching:** Rejected due to performance impact
- **Permanent Cache:** Rejected due to stale content risk
- **User-Controlled TTL:** Deferred to future version

---

## Decision 11: Documentation Generation

**Context:** Generated prototypes need user documentation.

**Decision:** Auto-generate comprehensive README with source attribution.

**README Includes:**
- Project overview
- Installation instructions (language-specific)
- Usage examples
- Source attribution with link
- License (MIT default)

**Rationale:**
- Users need context for generated code
- Installation steps vary by language
- Source attribution maintains traceability
- Complete documentation improves usability

**Alternatives Considered:**
- **Minimal README:** Rejected due to poor user experience
- **Separate Documentation:** Rejected; README is convention

---

## Decision 12: Language Support Priority

**Context:** Cannot support all programming languages initially.

**Decision:** Prioritize 5 languages with option to extend.

**Supported Languages:**
1. **Python** - ML, data science, general purpose
2. **JavaScript/TypeScript** - Web development
3. **Rust** - Systems programming
4. **Go** - Microservices, CLIs
5. **Julia** - Scientific computing

**Selection Rationale:**
- Cover major development domains
- Large user bases
- Mature ecosystems
- Distinct use cases

**Future Additions:**
- Java (enterprise)
- C++ (performance)
- Swift (iOS)
- Kotlin (Android)

---

## Decision 13: AgentDB Integration

**Context:** Skill should improve with usage (learning).

**Decision:** Design for AgentDB integration, implement gracefully without it.

**Integration Points:**
- Store successful patterns
- Query for similar past articles
- Learn optimal language mappings
- Validate decisions with historical data

**Rationale:**
- Progressive improvement over time
- Benefits from Agent-Skill-Creator ecosystem
- Works perfectly without AgentDB (fallback)
- Future-proofed for learning capabilities

**Implementation Note:**
Current v1.0 includes AgentDB interfaces but doesn't require AgentDB to function.

---

## Decision 14: Project Structure Conventions

**Context:** Generated projects should follow community standards.

**Decision:** Follow language-specific conventions strictly.

**Examples:**
- **Python:** `src/` for code, `tests/` for tests, PEP 8 style
- **JavaScript:** `index.js` entry point, `node_modules/` ignored
- **Rust:** `src/main.rs`, `Cargo.toml`, edition 2021
- **Go:** `main.go` in root, `go.mod` for dependencies

**Rationale:**
- Users expect familiar structures
- Tools work better with conventions
- Reduces cognitive load
- Enables immediate IDE integration

---

## Future Considerations

### Potential Enhancements

1. **Interactive Mode:** Ask user questions during generation
2. **Batch Processing:** Process multiple articles in parallel
3. **Incremental Updates:** Update existing prototypes with new articles
4. **Custom Templates:** User-defined generation templates
5. **More Languages:** Java, C++, Swift, Kotlin support
6. **Diagram Extraction:** Parse and implement architecture diagrams
7. **Video Transcripts:** Extract from video tutorials
8. **API Client Generation:** Auto-generate API clients from docs

### Performance Improvements

1. **Parallel Extraction:** Process long PDFs in parallel
2. **Streaming Analysis:** Analyze content as it's extracted
3. **Pre-compiled Patterns:** Cache regex compilation
4. **Incremental Generation:** Generate files in parallel

---

## Lessons Learned

### What Worked Well

- **Modular Architecture:** Easy to test and extend
- **Format-Specific Extractors:** Better quality than universal approach
- **Rule-Based Analysis:** Fast and deterministic
- **Template Generation:** Consistent, high-quality output

### What Could Be Improved

- **Algorithm Detection:** Still misses complex pseudocode
- **Dependency Resolution:** Could be more intelligent
- **Test Generation:** Too generic, needs domain-specific tests
- **Error Messages:** Could provide more specific troubleshooting

### What We'd Do Differently

- **Earlier Testing:** More test articles during development
- **Language Plugins:** More extensible language support architecture
- **Streaming Output:** Progress updates during long operations
- **Configuration System:** More user-configurable options

---

**Document Version:** 1.0
**Last Updated:** 2025-10-23
**Author:** Agent-Skill-Creator v2.1