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Complete restructure from patterns/examples/reference to technology-focused directories: • Created technology-specific directories with comprehensive documentation: - /tdarr/ - Transcoding automation with gaming-aware scheduling - /docker/ - Container management with GPU acceleration patterns - /vm-management/ - Virtual machine automation and cloud-init - /networking/ - SSH infrastructure, reverse proxy, and security - /monitoring/ - System health checks and Discord notifications - /databases/ - Database patterns and troubleshooting - /development/ - Programming language patterns (bash, nodejs, python, vuejs) • Enhanced CLAUDE.md with intelligent context loading: - Technology-first loading rules for automatic context provision - Troubleshooting keyword triggers for emergency scenarios - Documentation maintenance protocols with automated reminders - Context window management for optimal documentation updates • Preserved valuable content from .claude/tmp/: - SSH security improvements and server inventory - Tdarr CIFS troubleshooting and Docker iptables solutions - Operational scripts with proper technology classification • Benefits achieved: - Self-contained technology directories with complete context - Automatic loading of relevant documentation based on keywords - Emergency-ready troubleshooting with comprehensive guides - Scalable structure for future technology additions - Eliminated context bloat through targeted loading 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
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Python Patterns
Project Structure
- Use virtual environments (venv)
- Implement proper package structure with
__init__.py - Separate configuration from code
- Use requirements.txt for dependencies
Code Organization
- MVC/MVT patterns for web applications
- Factory pattern for object creation
- Context managers for resource handling
- Async/await for I/O-bound operations
Error Handling
- Use specific exception types
- Implement proper logging with levels
- Graceful degradation for external dependencies
- Validation at API boundaries
Performance Considerations
- Use generators for large datasets
- Profile before optimizing
- Leverage caching appropriately
- Consider async for concurrent operations
Related Documentation
- Examples:
/examples/python/web-frameworks.md - Examples:
/examples/python/api-clients.md - Examples:
/examples/python/async-patterns.md - Reference:
/reference/python/debugging.md - Reference:
/reference/python/performance.md