Developer Documentation
Technical information and guidelines for contributors to the AI Instruction Kits project.
🏗️ Architecture
System Configuration
- Instruction System: Reusable instructions organized by category
- Modular System: Innovative mechanism for dynamically generating instructions
- Checkpoint Feature: Automatic work progress tracking
Directory Structure
AI_Instruction_Kits/
├── instructions/ # Traditional instructions
├── modular/ # Modular system
│ ├── ja/ # Japanese modules
│ └── en/ # English modules
├── scripts/ # Utility scripts
└── docs/ # Documentation
└── developers/ # Developer documentation
└── research/ # Research materials & best practices
📚 Development Guide
Module Creation Best Practices
A practical guide for creating new modules. It introduces efficient development methods based on implementation experience from January 2025.
Key Contents:
- Parallel investigation strategies
- Quality assurance checklists
- Implementation examples and templates
Modular System Development Guide
Detailed development documentation for the modular system.
Key Contents:
- Module type descriptions
- Development process (6 phases)
- Category-specific guidelines
🔬 Technical References
Best Practice Materials by Specialty
Our project investigates the latest technology trends in each specialty field and uses them as reference materials for module development.
📖 Available Reference Materials
- Legal Engineering
- Differences between Legal Tech and Legal Engineering
- Agile Legal and DevOps for Law
- Latest trends 2024-2025
- Software Engineering
- SWEBOK v4 compliant best practices
- Sustainable development and accessibility
- Latest methods for AI-assisted development
- Parallel and Distributed Computing
- GPU/CUDA optimization techniques
- Cloud-native architecture
- Edge computing integration
- Machine Learning & AI
- MLOps best practices
- Responsible AI implementation
- Latest algorithms and frameworks
📖 Research Materials & Best Practices
Research Materials
Detailed research results and best practice documents for each field.
Available Materials:
- Module Creation: MODULE_CREATION_BEST_PRACTICES
- Context Optimization: context_optimization_best_practices_2025
- Academic Modules: academic_writing, citation_management, etc.
- Technical Fields: Detailed versions of software_engineering, machine_learning, etc.
These materials are utilized as important references when creating expertise modules.
🛠️ Development Environment Setup
Required Tools
# Basic tools
- Git
- Bash
- Python 3.8 or higher (for modular system)
# Recommended tools
- VS Code or any editor
- GitHub CLI (gh)
Initial Setup
# Clone repository
git clone https://github.com/dobachi/AI_Instruction_Kits.git
cd AI_Instruction_Kits
# Create development branch
git checkout -b feature/your-feature-name
🤝 Contribution
Adding New Modules
- Create new module in
modular/ja/modules/ - Add YAML metadata
- Create English version (
modular/en/modules/) - Run validation script
./scripts/validate-modules.sh - Fix any errors
- Add tests and documentation
Pull Request Guidelines
- Clear title and description
- Include related Issue numbers
- Confirm both Japanese and English updates
- Include test results
Commit Message Convention
<type>: <description>
- feat: Add new feature
- fix: Fix bug
- docs: Update documentation
- refactor: Refactoring
- test: Add/fix tests
📊 Quality Standards
Code Review Checklist
- No syntax errors
- Japanese-English consistency
- Accurate metadata
- Clear dependencies
- Working implementation examples
Test Requirements
- Module generation tests
- Integration tests
- Documentation consistency check
🚀 Future Development Plans
Ongoing Projects
- Modular system expansion
- Automated test framework
- Web UI development
Areas Needing Contribution
- Expertise modules for new specialties
- Multi-language support (Chinese, Korean, etc.)
- Performance optimization
📞 Communication
Questions & Discussion
Important Links
Thank you for contributing to the development!
Let's build better AI Instruction Kits together 🚀