feat(predict): enrich YAML sidecar with provenance and timing

`giant predict`'s sidecar previously stopped at kind/prediction_id/
output/dataset/checkpoint/timestamp, unlike `giant rollout`'s, which
carries full run provenance (model_config, training_epoch,
training_config, timing, ...) that flows into analysis gallery
metadata. `analyze --prediction` consumed the same thin sidecar, so a
prediction series in an analysis run was nearly unlabeled compared to
its rollout counterparts.

- `_write_prediction_ref` takes an `extra: dict | None` merged into
  the sidecar; `giant rollout` now uses it instead of a
  load/update/rewrite round trip (identical output).
- New `_build_predict_timing`, key-compatible with
  `_build_rollout_timing`, from timers now wrapping predict's setup/
  sample/write phases.
- `giant predict` writes coord, has_truth, schema_version, steps,
  weights, device, batch_size(+auto), row/skip/unknown-pdg counts,
  timing, and the checkpoint's model_config/config_overrides/
  training_epoch/best_val_loss/training_config/training_meta.
- `giant/analysis/condor.py`'s `_PLOT_META_KEYS` forwards the new
  predict-only keys (plus rollout's previously-unforwarded
  config_overrides) into each plot's gallery metadata.yaml.
- Fixes a `ty` regression from the prior commit in
  tests/test_cli_predict.py (Command has no static `.commands`).

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01PpxE9nij3ujg9XcuzvQ26q

tests/test_condor.py

@@ -256,6 +256,46 @@ def test_prep_with_prediction_writes_run_meta(tmp_path: Path):
     assert partial.data["available"]
 
 
+def test_prep_forwards_predict_only_metadata_keys(tmp_path: Path):
+    """A rich `giant predict` sidecar's provenance/timing keys reach
+    run_meta.json's plot_meta, same as a rollout's do — a thin legacy
+    sidecar (no such keys) still loads fine (see _write_prediction_yaml)."""
+    rollout_yaml = _write_inputs(tmp_path)
+    reference = load_rollout_yaml(rollout_yaml)["dataset"]
+    pred = tmp_path / "pred_rich.parquet"
+    _write_prediction(pred, coord="global")
+    yaml_path = tmp_path / "pred_rich.yaml"
+    yaml_path.write_text(
+        yaml.safe_dump(
+            {
+                "kind": "prediction",
+                "prediction_id": "richpred12",
+                "output": str(pred),
+                "dataset": str(reference),
+                "checkpoint": "/ckpt/rich.pt",
+                "coord": "global",
+                "has_truth": True,
+                "schema_version": "3",
+                "n_input_rows": 1000,
+                "n_files": 1,
+                "n_skipped_rows": 3,
+                "unknown_pdg_counts": {"999999": 3},
+                "batch_size_auto": False,
+                "timing": {"us_per_step": 12.5},
+            }
+        )
+    )
+    run_dir = _prep([rollout_yaml], prediction_yamls=[yaml_path])
+    meta = RunMeta.load(run_dir / "run_meta.json")
+    plot_meta = meta.predictions[0]["plot_meta"]
+    assert plot_meta["coord"] == "global"
+    assert plot_meta["has_truth"] is True
+    assert plot_meta["n_input_rows"] == 1000
+    assert plot_meta["n_skipped_rows"] == 3
+    assert plot_meta["unknown_pdg_counts"] == {"999999": 3}
+    assert plot_meta["timing"] == {"us_per_step": 12.5}
+
+
 def test_derive_run_dir_next_to_rollout():
     y = {"output": "/data/roll.parquet", "prediction_id": "abcd1234ef", "dataset": "d"}
     assert derive_run_dir([y]) == Path("/data/analysis_abcd1234")