Comprehensive error handling improvements addressing inconsistent patterns:
• Created markitect/exceptions.py with complete domain-specific exception hierarchy
- MarkitectError base class with context and cause chaining support
- Specific exceptions for Document, AST, Cache, Database, Schema operations
- Built-in logging and context preservation
• Fixed overly broad exception handling in tddai modules:
- issue_fetcher.py: Replace generic Exception with specific Gitea errors
- project_manager.py: Proper error translation with context preservation
- coverage_analyzer.py: Replace silent suppression with logging
• Enhanced cache_service.py error handling:
- Specific OSError/PermissionError handling for file operations
- Logging integration for unexpected errors
- Preserved error collection and reporting
• Implemented proper exception chaining patterns:
- All error translations use `raise ... from e` for debugging
- Preserved original exception context and stack traces
- Added docstring declarations of raised exceptions
• Benefits:
- Eliminates silent error suppression and debugging black holes
- Provides specific, actionable error messages
- Preserves full error context for troubleshooting
- Establishes consistent patterns for future development
Resolves issue #21: Error handling standardization
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
Added two new Claude Code subagents following proper specification format:
**Kaizen Optimizer Agent:**
- Meta-agent for analyzing and optimizing other subagents
- Performance analysis and specification improvement recommendations
- Agent ecosystem health assessment and continuous improvement
- Proper YAML frontmatter with proactive usage guidelines
**Refactoring Assistant Agent (Optimized):**
- Streamlined from 19-section complex specification to focused Claude Code format
- Code quality assessment and refactoring guidance within Claude Code environment
- Security analysis and performance optimization recommendations
- Integration with existing agent ecosystem (tddai-assistant, general-purpose, project-assistant)
**Also includes Issue #15 AST Query CLI implementation:**
- AST Service with display, query, and statistics capabilities
- JSONPath integration for flexible AST navigation
- CLI commands: ast-show, ast-query, ast-stats (22/22 tests passing)
- Leverages existing cache system for optimal performance
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
Implement comprehensive database query interface with multiple output formats:
• Add query command for executing read-only SQL queries with security constraints
• Add schema command for database structure inspection
• Add metadata command for file information display
• Support table, JSON, and YAML output formats across all commands
• Implement SQL injection prevention and safety checks
• Add tabulate dependency for enhanced table formatting
• Create 35 comprehensive tests covering all functionality
This delivers the core USP "Relational Document Metadata" by making the
database fully queryable through CLI commands with multiple output formats.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
Complete CLI implementation using Click framework with core commands:
- ingest: Process and store markdown files with progress feedback
- status: Display file processing status and metadata
- list: Show all stored files with optional verbose details
Features:
- Global options (--verbose, --config, --database)
- Comprehensive error handling and user-friendly output
- Integration with existing DatabaseManager and DocumentManager
- Proper console script configuration in pyproject.toml
- Extensive inline documentation and help text
- Robust front matter parsing with error handling
Technical Implementation:
- Added Click dependency (>=8.0.0) to pyproject.toml
- Console script entry point: markitect.cli:main
- Full integration with database and caching systems
- Performance-aware implementation maintaining existing architecture
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
Complete TDD implementation of core MarkiTect functionality:
**Database Module (markitect/database.py):**
- DatabaseManager class with SQLite database initialization
- markdown_files table with proper schema (id, filename, front_matter, content, created_at)
- Front matter storage as JSON with content separation
- File storage, retrieval, and listing methods
- Comprehensive error handling
**Front Matter Module (markitect/frontmatter.py):**
- FrontMatterParser class with YAML front matter parsing
- Clean separation of metadata from markdown content
- Graceful handling of invalid YAML and missing front matter
- Regex-based parsing with proper delimiter handling
**Dependencies:**
- Added PyYAML for front matter parsing
- Updated pyproject.toml with new dependency
**Test Coverage:**
- 9 comprehensive tests covering all functionality
- Database initialization and schema validation
- Front matter parsing with Issue #1 example content
- Integrated workflow testing (storage/retrieval)
- Error handling for edge cases
**TDD Process:**
- RED phase: 8 failing tests defining requirements
- GREEN phase: Minimal implementation making all tests pass
- Validation: Complete workflow verified with example content
This implementation provides the foundation for all subsequent MarkiTect
features, handling the exact example from Issue #1 specification.
Issue #1: Initialize Database and Store Example Markdown File
coulomb/markitect_project#1🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
