feat(infospace): add eval-summary command and improve evaluate pipeline (S3.3)

- Fix evaluate dimensions to match template file:
  definition_precision, source_grounding, domain_placement,
  vsm_relevance, explanatory_value (was domain_relevance,
  discipline_alignment, conceptual_clarity)
- Add VSM background context to evaluation prompt so LLM can
  score vsm_relevance without macro injection
- Fix model_name bug: was sending literal "default" to API (HTTP 400)
- Refactor run_entity_evaluation to write files incrementally via
  callback rather than all at once after the batch — long runs are
  now resumable if interrupted
- Add incremental skip in CLI: entities with existing eval files
  are skipped automatically on re-run (acts as resume)
- Add eval-summary command: reads all eval files, shows per-dimension
  means, optionally writes per_entity_mean to metrics.yaml
- Fix record_check_results to merge rather than overwrite metrics.yaml
  so per_entity_mean survives subsequent check runs
- Add per_entity_mean viability threshold (min: 3.5) to infospace.yaml

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

markitect/infospace/cli.py

@@ -189,11 +189,26 @@ def evaluate(config_path, provider, model, entity_slug, chapter):
             click.echo(f"No entities found for chapter '{chapter}'.")
             return
 
+    # Skip entities that already have evaluation files (incremental resume)
+    from markitect.infospace.evaluate import run_entity_evaluation
+    output_dir = root / cfg.evaluations_dir
+    if not entity_slug and not chapter and output_dir.is_dir():
+        previous_digests = {
+            p.stem: ""  # non-empty sentinel → triggers skip in BatchEvaluator
+            for p in output_dir.glob("*.md")
+        }
+        entity_list = [e for e in entity_list if e.slug not in previous_digests]
+        if not entity_list:
+            click.echo("All entities already evaluated. Nothing to do.")
+            return
+        if previous_digests:
+            click.echo(f"Skipping {len(previous_digests)} already-evaluated entities.")
+
     # Create adapter
     from markitect.llm import create_adapter
     from markitect.prompts.execution.models import RunConfig
     adapter = create_adapter(provider, model=model)
-    run_config = RunConfig(model_name=model or "default", temperature=0.3, max_tokens=2000)
+    run_config = RunConfig(model_name=model, temperature=0.3, max_tokens=2000)
 
     # Progress callback
     def on_progress(done, total, result):
@@ -202,8 +217,6 @@ def evaluate(config_path, provider, model, entity_slug, chapter):
 
     click.echo(f"Evaluating {len(entity_list)} entities via {provider}...")
 
-    from markitect.infospace.evaluate import run_entity_evaluation
-    output_dir = root / cfg.evaluations_dir
     summary = run_entity_evaluation(
         config=cfg,
         entities=entity_list,
@@ -218,6 +231,74 @@ def evaluate(config_path, provider, model, entity_slug, chapter):
         click.echo(f"Tokens used: {summary.total_tokens}")
 
 
+# ── eval-summary ──────────────────────────────────────────────────────
+
+
+@infospace_commands.command(name="eval-summary")
+@click.option("--config", "config_path", default=None, help="Path to infospace.yaml.")
+@click.option("--update-metrics", is_flag=True, default=False,
+              help="Merge per_entity_mean into metrics.yaml for viability checks.")
+def eval_summary(config_path: Optional[str], update_metrics: bool):
+    """Show aggregate statistics from per-entity evaluation files."""
+    cfg, cfg_path = _load_config_or_exit(config_path)
+    root = cfg_path.parent
+
+    evaluations_dir = root / cfg.evaluations_dir
+    if not evaluations_dir.is_dir():
+        click.echo("No evaluations directory found. Run 'markitect infospace evaluate' first.")
+        return
+
+    from markitect.infospace.evaluation_io import read_entity_evaluation
+
+    eval_files = sorted(evaluations_dir.glob("*.md"))
+    if not eval_files:
+        click.echo("No evaluation files found.")
+        return
+
+    overall_scores: list = []
+    dim_scores: dict = {}
+    failed: list = []
+
+    for ef in eval_files:
+        try:
+            ev = read_entity_evaluation(ef)
+            overall_scores.append(ev.overall_score)
+            for s in ev.scores:
+                dim_scores.setdefault(s.name, []).append(s.value)
+        except Exception as exc:
+            failed.append((ef.stem, str(exc)))
+
+    n = len(overall_scores)
+    if n == 0:
+        click.echo("No evaluations could be read.")
+        return
+
+    mean_overall = sum(overall_scores) / n
+
+    click.echo(f"Evaluation summary — {n} entities evaluated")
+    if failed:
+        click.echo(f"  (failed to read: {len(failed)})")
+    click.echo()
+    click.echo(f"  {'Dimension':<30} {'Mean':>6}")
+    click.echo("  " + "-" * 38)
+    click.echo(f"  {'overall':<30} {mean_overall:>6.3f}")
+    for dim, vals in sorted(dim_scores.items()):
+        click.echo(f"  {dim:<30} {sum(vals)/len(vals):>6.3f}")
+
+    score_min = min(overall_scores)
+    score_max = max(overall_scores)
+    click.echo()
+    click.echo(f"  Range: {score_min:.2f} – {score_max:.2f}")
+
+    if update_metrics:
+        from markitect.infospace.history import read_metrics_file, write_metrics_file
+        metrics_file = root / cfg.metrics_dir / "metrics.yaml"
+        existing = read_metrics_file(metrics_file)
+        existing["per_entity_mean"] = round(mean_overall, 6)
+        write_metrics_file(existing, metrics_file)
+        click.echo(f"\nUpdated metrics.yaml: per_entity_mean = {mean_overall:.4f}")
+
+
 # ── viability ────────────────────────────────────────────────────────