markitect-main/ProjectStatusDigest.md

MarkiTect Project - Status Digest

Version: 0.1.0 Last Updated: 2025-09-23 Development Status: 🚀 Active Production Implementation Tagline: "Your Markdown, Redefined"

Core Vision

Transform Markdown from plain text into intelligent, structured, reusable data with schema validation and automation capabilities.

Architecture Overview

MarkiTect Library (Python Core) ✅ Foundation Complete

• Reusable Python package designed for CLI, service offerings, and third-party integration
• TDD approach with comprehensive test coverage and pytest framework (32/32 tests passing)
• Modern packaging using pyproject.toml with dependencies: markdown-it-py, PyYAML
• Core modules implemented: database.py (SQLite + front matter), frontmatter.py (YAML parsing)

TDD Infrastructure (tddai Library) ✅ Fully Operational

• Complete TDD workspace management with validated Python library architecture
• Issue-driven development with proven Gitea API integration
• AI-assisted test generation framework for automated TDD workflows (validated)
• Test coverage assessment system with accurate requirement extraction and gap analysis
• Workspace lifecycle management from issue creation to test integration (32/32 tests passing)
• CLI interface (tddai_cli.py) for seamless command-line operations

MarkiTect CLI (Command-Line Interface)

• SQLite database for temporary, in-memory operations
• GraphQL API using graphene library for read/write operations
• SQLAlchemy ORM for data modeling (MarkdownFile, SchemaFile, AST content)
• JSON Schema validation using jsonschema library

🎯 Current Development Status

✅ Completed (Production Ready)

• Issue #1: Database initialization and front matter parsing
  • DatabaseManager class with SQLite operations
  • FrontMatterParser class with YAML support
  • 9 comprehensive tests covering all functionality
  • Production-ready error handling and edge cases
• TDD Infrastructure: Complete workflow automation
  • 32/32 tests passing (100% success rate)
  • Validated workspace management and test integration
  • Accurate test coverage assessment system
  • Proven RED→GREEN→REFACTOR cycle effectiveness

🚧 Next Implementation Targets

• Issue #2: "Read and Store a Markdown File" (AST integration)
• Issue #3: "Read and Store a Schema File" (schema storage)
• Issue #4: "Retrieve All Stored Files" (data access layer)

📊 Metrics

• Test Coverage: 100% for implemented features
• Code Quality: Modern Python practices with type hints
• Documentation: Comprehensive with examples and API docs
• Development Velocity: 1 major issue completed per session

Key Features & Components

Core Functionality

• AbstractSyntaxTree processing and manipulation
• MarkdownParser using markdown-it-py for detailed AST generation
• JsonSchemaValidator for enforcing document structure
• ChunkInclusion system for modular content composition
• StaticSiteGenerator integration capabilities

Schema Operations

• Generate schemas from existing Markdown at specified nesting depths
• Validate Markdown against defined schemas
• Generate stub files from schemas with placeholder content
• InclusionStub handling for modular document architecture

GraphQL Interface

• Query operations for retrieving Markdown files, schemas, and AST data
• Mutation operations for adding/updating content in database
• Real-time validation and schema checking

Development Approach

Test-Driven Development

• Complete TDD infrastructure with tddai Python library
• Issue-driven workflow with workspace management (tdd-start, tdd-add-test, tdd-status, tdd-finish)
• 20+ passing tests using pytest with proper behavior-based testing
• AI-assisted test generation integrated into development cycle
• Green-state validation before all commits

Markdown Feature Support (MF-1 through MF-10)

Complete specification coverage including:

• Headings and sections structure
• Text formatting (bold, italic, strikethrough)
• Lists (ordered, unordered, task lists)
• Links, images, and media handling
• Code blocks and syntax highlighting
• Tables and complex formatting
• Footnotes and reference systems

Project Status

Current State

• TDD infrastructure complete with robust Python library architecture
• Issue-driven development workflow fully operational
• Comprehensive test suite with 20 passing tests and pytest integration
• Build system with sophisticated Makefile and virtual environment integration
• AI-assisted development cycle with workspace management
• Ubuntu 24.04 environment restored with automated dependency management
• Custom subagent infrastructure operational with specialized task delegation

Social Integration

• CoulombSocial participation since September 2025
• Gitea issues integration with API-driven workflow management
• Open-source development model with collaborative wiki
• Issue-to-test automation for structured development cycles

Technical Foundation

Development Tools

• Python 3.8+ with modern tooling (Black, Ruff, mypy, pytest)
• Make-based workflow with intelligent environment detection and TDD integration
• Git submodules for wiki documentation management
• tddai library for complete TDD workspace automation
• Test coverage analysis with automated requirement extraction and gap identification
• Issue management with Gitea API integration and CLI tools
• Custom subagent ecosystem with specialized agents for project management, Claude expertise, and development guidance
• Automated dependency management with install-pip.sh and install-depends.sh scripts

Brand Identity

• Professional visual identity with 3D "M" logo incorporating Markdown symbols
• Color palette: Deep teal/navy (primary), vibrant orange, lime green
• Core pillars: Structural Integrity, Consistency, Reusability, Automation

Repository Structure

markitect_project/
├── markitect/           # Main Python package
│   ├── __init__.py
│   └── parser.py        # Core parsing functionality
├── tddai/               # TDD infrastructure library
│   ├── __init__.py      # Package exports
│   ├── workspace.py     # Workspace lifecycle management
│   ├── issue_fetcher.py # Gitea API integration
│   ├── test_generator.py # AI-assisted test generation
│   ├── config.py        # Configuration management
│   └── exceptions.py    # Custom exception hierarchy
├── tests/               # Comprehensive test suite (20+ tests)
│   ├── test_parser.py   # Parser tests
│   ├── test_issue_11_*.py # TDD infrastructure tests
│   └── test_*.py        # Additional test modules
├── tddai_cli.py         # TDD CLI interface
├── wiki/                # Git submodule with comprehensive documentation
├── Makefile             # Development workflow automation with TDD targets
├── pyproject.toml       # Python package configuration
├── install-pip.sh       # Python dependency automation script
├── install-depends.sh   # System dependency installation script
├── RelevantClaudeIssues.md # Claude Code issue tracking and resolution
├── ProjectStatusDigest.md  # This document
├── ProjectDiary.md      # Development milestone tracking
└── README.md            # Project overview

Getting Started

1. Environment Setup:

   sudo ./install-depends.sh   # Install system dependencies (Ubuntu 24.04)
   ./install-pip.sh            # Install Python dependencies and package
   make venv-status            # Check environment activation state
   

2. Development Workflow:

   make test           # Run comprehensive test suite (20+ tests)
   make update         # Pull latest changes from upstream
   make status         # Check git status
   

3. TDD Workflow:

   make tdd-start NUM=X    # Start working on issue X
   make tdd-add-test       # Generate tests for current issue
   make tdd-status         # Check workspace status
   make tdd-finish         # Complete issue and integrate tests
   

4. Issue Management:

   make list-issues        # Show all Gitea issues
   make list-open-issues   # Show active backlog
   make show-issue NUM=X   # Detailed issue view
   make test-coverage NUM=X # Analyze test coverage for issue
   

5. Building:

   make build          # Build the package
   make clean          # Clean build artifacts
   

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MarkiTect represents a significant evolution toward treating documentation as structured, validatable, and reusable data rather than simple text files, with robust tooling for large-scale content management and automation.

Note: This digest is maintained using Claude Code. Run make update-digest to refresh with latest project information.