feat(infospace): add L2 entity classification with type × VSM matrix (S2.9)

Implements the L2 typed-entities layer — each entity is assigned an
Entity Type (Element, Process, Relation, Principle, Institution) and a
VSM System (S1–S5) by an LLM, with one-sentence rationales for each.

New modules:
- markitect/infospace/classification.py — EntityClassification dataclass
  + ENTITY_TYPES / VSM_SYSTEMS controlled vocabularies
- markitect/infospace/classification_io.py — write/read classification
  files (YAML frontmatter + markdown body, mirrors evaluation_io)
- markitect/infospace/classifier.py — build_classification_prompt(),
  parse_classification_response(), run_entity_classification(); batch
  runner writes files incrementally (same resumable pattern as evaluate)

CLI: markitect infospace classify [--entity SLUG] [--provider P] [--model M]
  - Incremental skip: checks output/classifications/ for existing files
  - Defaults to openrouter provider; 2000 max_tokens (Gemini 2.5 Flash
    uses ~787 thinking tokens, so 800 was too low)

CLI: markitect infospace classify-summary [--update-metrics]
  - Entity type counts + VSM system counts with percentages
  - 5 × 6 type × VSM matrix (spots structural blind spots at a glance)
  - --update-metrics writes type_distribution, type_entropy,
    vsm_type_matrix_cells to metrics.yaml

Config: InfospaceConfig gains classifications_dir (default output/classifications)
Schema: schemas/typed-entity-schema-v1.0.md — type/VSM vocabulary tables,
  rationale format rules, validation rules, metrics enabled at L2
infospace.yaml: schemas.typed_entity references typed-entity-schema-v1.0.md

Seed classifications (3): division_of_labour (Process/S1),
  natural_price_as_central_price (Principle/S2),
  invisible_hand_mechanism (Principle/S4)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

markitect/infospace/classification_io.py

@@ -0,0 +1,80 @@
+"""
+Read/write utilities for entity classification files (L2).
+
+Classification files use YAML frontmatter (machine-readable) plus a
+markdown body (human-readable), matching the convention used by evaluation
+files.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import List
+
+import yaml
+
+from .classification import EntityClassification
+
+
+_FRONTMATTER_SEP = "---"
+
+
+def write_entity_classification(c: EntityClassification, path: Path) -> None:
+    """Write a per-entity classification as YAML frontmatter + markdown body."""
+    fm = c.to_dict()
+
+    lines: List[str] = []
+    lines.append(_FRONTMATTER_SEP)
+    lines.append(yaml.safe_dump(fm, default_flow_style=False, sort_keys=False).rstrip())
+    lines.append(_FRONTMATTER_SEP)
+    lines.append("")
+
+    title = c.entity_slug.replace("_", " ").replace("-", " ").title()
+    lines.append(f"# Classification: {title}")
+    lines.append("")
+
+    lines.append("## Entity Type")
+    lines.append("")
+    lines.append(c.entity_type)
+    lines.append("")
+
+    lines.append("## VSM System")
+    lines.append("")
+    lines.append(c.vsm_system)
+    lines.append("")
+
+    if c.type_rationale:
+        lines.append("## Type Rationale")
+        lines.append("")
+        lines.append(c.type_rationale)
+        lines.append("")
+
+    if c.vsm_rationale:
+        lines.append("## VSM Rationale")
+        lines.append("")
+        lines.append(c.vsm_rationale)
+        lines.append("")
+
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text("\n".join(lines), encoding="utf-8")
+
+
+def read_entity_classification(path: Path) -> EntityClassification:
+    """Read a classification file (YAML frontmatter + markdown body)."""
+    text = path.read_text(encoding="utf-8")
+    parts = text.split(f"{_FRONTMATTER_SEP}\n", maxsplit=2)
+    if len(parts) < 3:
+        raise ValueError(f"No YAML frontmatter found in {path}")
+    fm = yaml.safe_load(parts[1])
+    return EntityClassification.from_dict(fm)
+
+
+def read_classifications_directory(directory: Path) -> List[EntityClassification]:
+    """Read all classification files from a directory."""
+    results: List[EntityClassification] = []
+    for p in sorted(directory.glob("*.md")):
+        try:
+            results.append(read_entity_classification(p))
+        except Exception:
+            pass
+    return results