feat(spaces): implement Phase 0-1 of Information Space Service

Phase 0 - Project Organization:
- Create docs/PROJECT_STRUCTURE.md documenting codebase layout
- Create markitect/core/ with parser, serializer, document_manager, workspace
- Create markitect/schema/ consolidating 6 schema_*.py modules
- Create markitect/storage/ with database module
- Maintain backward compatibility via re-exports from original locations
- Add docs/roadmap/information-space-service/ with README and WORKPLAN

Phase 1 - Foundation (Weeks 1-3):
- Week 1: Core domain models (InformationSpace, SpaceDocument, SpaceConfig,
  SpaceMetadata, SpaceVariable, TransclusionReference, SpaceStatus)
- Week 2: Repository layer with interfaces (ISpaceRepository,
  IDocumentAssociationRepository, IVariableRepository, IReferenceRepository)
  and SQLite implementations with foreign key cascade deletes
- Week 3: SpaceService orchestration layer with full CRUD, document,
  variable, and reference tracking operations

Test coverage: 124 tests (25 model + 63 repository + 36 integration)

Capabilities delivered:
- CAP-001: InformationSpace entity with lifecycle management
- CAP-002: SpaceRepository CRUD with SQLite backing
- CAP-003: Document-Space associations with path-based organization
- CAP-004: Space metadata and configuration schemas
- CAP-005: Database schema with migrations

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

markitect/core/parser.py

@@ -0,0 +1,47 @@
+"""
+Markdown AST Parser.
+
+This module provides functionality to parse markdown content into an
+Abstract Syntax Tree (AST) using the markdown-it library.
+"""
+
+from markdown_it import MarkdownIt
+
+
+def parse_markdown_to_ast(md_content: str):
+    """
+    Parse markdown content into a JSON-serializable AST.
+
+    Args:
+        md_content: Markdown text to parse
+
+    Returns:
+        List of token dictionaries representing the AST
+
+    Example:
+        ast = parse_markdown_to_ast("# Hello\\n\\nWorld")
+    """
+    # Enable table parsing and other common plugins
+    md = MarkdownIt("commonmark", {"tables": True}).enable(['table'])
+    tokens = md.parse(md_content)
+
+    # Convert to a JSON-serializable list of dicts
+    def token_to_dict(token):
+        d = {
+            'type': token.type,
+            'tag': token.tag,
+            'attrs': token.attrs,
+            'map': token.map,
+            'nesting': token.nesting,
+            'level': token.level,
+            'children': [token_to_dict(child) if child else None for child in token.children] if token.children else None,
+            'content': token.content,
+            'markup': token.markup,
+            'info': token.info,
+            'meta': token.meta,
+            'block': token.block,
+            'hidden': token.hidden
+        }
+        return {k: v for k, v in d.items() if v is not None}  # Remove None values
+
+    return [token_to_dict(token) for token in tokens]