gh-francyjglisboa-agent-skill-creator/article-to-prototype-cskill/DECISIONS.md

Architectural Decisions

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

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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

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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:

# 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

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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

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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

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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

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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

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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

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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:

# 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

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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)

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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

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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

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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)

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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.

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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

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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

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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

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