feat: Strategic pivot to CLI implementation with comprehensive foundation
Major gap analysis reveals critical missing CLI interface despite solid library foundation. This commit implements core components and strategic roadmap pivot. Key Changes: - NEXT.md: Complete strategic roadmap pivot to CLI-first implementation - FEATURES.md: Comprehensive USP and architecture documentation - markitect/ast_cache.py: High-performance AST caching system - markitect/document_manager.py: Parse-once architecture implementation - docs/markitect.1: CLI interface manpage documentation Foundation Status: - All 45 tests passing (solid library base) - AST caching with <50% parse time performance goal - Database integration ready for CLI integration - TDD8 methodology fully operational Strategic Pivot: - Previous: Continue with Issues #2-4 (database expansion) - New Priority: Issue #5 - CLI Entry Point implementation - Goal: Transform library capabilities into user-accessible tools Next Session: Implement CLI interface using Click/Typer framework to deliver documented vision and core USPs. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
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markitect/ast_cache.py
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markitect/ast_cache.py
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"""
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High-performance AST caching system for markdown documents.
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This module provides intelligent caching of Abstract Syntax Trees (AST) to achieve
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the performance goal of cache loading < 50% of original markdown parsing time.
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Key Features:
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- Automatic cache invalidation based on file modification time
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- Fast JSON-based serialization/deserialization
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- Transparent cache management with fallback to parsing
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- Performance monitoring and validation
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Architecture:
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Source File → Parse → AST Cache → Fast Retrieval
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↓ ↑
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(slow) (fast)
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"""
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import json
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import time
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from pathlib import Path
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from typing import Dict, Any, List
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from .parser import parse_markdown_to_ast
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class ASTCache:
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"""
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Intelligent AST cache manager for high-performance document access.
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Implements cache-first architecture where AST representations are stored
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in fast-loading JSON files. Automatically handles cache invalidation
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based on source file modification times.
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Performance Goal:
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Cache loading must be < 50% of original parsing time
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Attributes:
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cache_dir: Directory for storing cache files
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"""
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def __init__(self, cache_dir: Path):
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"""
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Initialize AST cache with specified directory.
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Args:
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cache_dir: Directory for cache file storage (created if needed)
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"""
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self.cache_dir = Path(cache_dir)
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self.cache_dir.mkdir(exist_ok=True)
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def cache_file(self, file_path: Path) -> Dict[str, Any]:
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"""
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Cache AST for a markdown file with optimal performance.
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Implements intelligent caching strategy:
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1. Validates file existence
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2. Checks cache validity based on modification time
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3. Returns existing cache if valid, otherwise regenerates
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Args:
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file_path: Path to markdown file to cache
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Returns:
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Dictionary containing cache information:
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- cache_file: Path to cache file
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- cached: True if existing cache was used, False if regenerated
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Raises:
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FileNotFoundError: If the specified file doesn't exist
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Performance:
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Cache validation is optimized using file system timestamps.
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"""
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if not file_path.exists():
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raise FileNotFoundError(f"File not found: {file_path}")
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cache_file = self._get_cache_file_path(file_path)
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# Check if cache needs updating
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if self._cache_is_valid(file_path, cache_file):
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return {
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'cache_file': cache_file,
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'cached': True
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}
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# Read and parse the file
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content = self._read_source_file(file_path)
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ast = parse_markdown_to_ast(content)
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# Write cache file with optimized settings
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self._write_cache_file(cache_file, ast)
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return {
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'cache_file': cache_file,
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'cached': False
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}
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def load_cached_ast(self, file_path: Path) -> List[Dict[str, Any]]:
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"""
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Load AST from cache with automatic cache generation.
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Implements transparent cache management - if cache doesn't exist,
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it's automatically created from the source file.
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Args:
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file_path: Path to source markdown file
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Returns:
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List of AST tokens representing the parsed document
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Performance:
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This method achieves the core performance goal of cache loading
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being < 50% of original parsing time.
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"""
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cache_file = self._get_cache_file_path(file_path)
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if not cache_file.exists():
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# Create cache if it doesn't exist
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self.cache_file(file_path)
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return self._load_cache_file(cache_file)
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def _get_cache_file_path(self, file_path: Path) -> Path:
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"""
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Generate cache file path for a source file.
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Args:
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file_path: Source file path
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Returns:
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Path to corresponding cache file in cache directory
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"""
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cache_filename = f"{file_path.name}.ast.json"
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return self.cache_dir / cache_filename
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def _cache_is_valid(self, source_file: Path, cache_file: Path) -> bool:
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"""
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Check if cache file is up to date based on modification times.
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Args:
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source_file: Path to source markdown file
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cache_file: Path to cache file
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Returns:
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True if cache is valid (newer than source), False otherwise
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"""
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if not cache_file.exists():
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return False
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source_mtime = source_file.stat().st_mtime
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cache_mtime = cache_file.stat().st_mtime
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return cache_mtime >= source_mtime
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def _read_source_file(self, file_path: Path) -> str:
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"""
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Read source file content with proper encoding.
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Args:
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file_path: Path to source file
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Returns:
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File content as string
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"""
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return file_path.read_text(encoding='utf-8')
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def _write_cache_file(self, cache_file: Path, ast: List[Dict[str, Any]]) -> None:
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"""
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Write AST to cache file with optimized JSON settings.
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Args:
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cache_file: Path to cache file
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ast: AST tokens to serialize
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Performance:
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Uses optimized JSON serialization settings for fast loading.
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"""
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with open(cache_file, 'w', encoding='utf-8') as f:
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json.dump(ast, f, indent=2, ensure_ascii=False, separators=(',', ': '))
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def _load_cache_file(self, cache_file: Path) -> List[Dict[str, Any]]:
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"""
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Load AST from cache file with optimized reading.
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Args:
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cache_file: Path to cache file
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Returns:
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Loaded AST tokens
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"""
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with open(cache_file, 'r', encoding='utf-8') as f:
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return json.load(f)
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