refactor(metadata): remove vestigial top-level trainedWords field

The field dates back to a development-stage bug in the enrich-metadata
(agent) pipeline, which briefly wrote trigger words at the top level of
model metadata instead of the established civitai.trainedWords location.
The write path was fixed before the feature merged to main (PR #1013)
and never shipped in any release, so no writer has existed since.

Remove the leftover pieces:

- BaseModelMetadata.trainedWords field (py/utils/models.py); sidecars
  from that dev window now pass the key through _unknown_fields instead
- HF download handler's strip-empty-trainedWords special case, reverting
  to saving the metadata object directly (py/routes/handlers/hf_handlers.py)
- trainedWords in the LLM enrichment context (agent_service.py)
- matching fallbacks/fixtures in the enrich_hf_validation harness and
  post-processor test

Trigger words continue to live in civitai.trainedWords for all model
sources, which is what the UI, agent post-processor, and metadata sync
all read and write.

py/routes/handlers/hf_handlers.py

@@ -122,12 +122,8 @@ async def _save_hf_metadata(dest_path: str, repo: str, model_root: str) -> None:
         metadata._unknown_fields["hf_url"] = hf_url
         metadata.from_civitai = False  # HF models are not from CivitAI
 
-        metadata_dict = metadata.to_dict()
-        if "trainedWords" in metadata_dict and not metadata_dict["trainedWords"]:
-            del metadata_dict["trainedWords"]
-
         # 3. Save metadata atomically
-        await MetadataManager.save_metadata(dest_path, metadata_dict)
+        await MetadataManager.save_metadata(dest_path, metadata)
         logger.info("Saved HF metadata (with hf_url) for %s", dest_path)
 
         # 4. Determine relative folder path for cache

py/services/agent/agent_service.py

@@ -407,7 +407,6 @@ class AgentService:
             "base_model": metadata.get("base_model", ""),
             "tags": metadata.get("tags", []),
             "modelDescription": metadata.get("modelDescription", ""),
-            "trainedWords": metadata.get("trainedWords", []),
             "sha256": (metadata.get("sha256") or "")[:16] + "..." if metadata.get("sha256") else "",
             "size": metadata.get("size", 0),
         }

py/utils/models.py

@@ -77,9 +77,6 @@ class BaseModelMetadata:
     last_checked_at: float = 0  # Last checked timestamp
     hash_status: str = "completed"  # Hash calculation status: pending | calculating | completed | failed
     autov3: Optional[str] = None  # CivitAI AutoV3 hash (12-char lowercase hex); "" = checked but unavailable, None = not checked
-    trainedWords: List[str] = field(
-        default_factory=list
-    )  # Trigger words / activation prompts (source-agnostic)
     _unknown_fields: Dict[str, Any] = field(
         default_factory=dict, repr=False, compare=False
     )  # Store unknown fields
@@ -92,9 +89,6 @@ class BaseModelMetadata:
         if self.tags is None:
             self.tags = []
 
-        if self.trainedWords is None:
-            self.trainedWords = []
-
     @classmethod
     def from_dict(cls, data: Dict[str, Any]) -> "BaseModelMetadata":
         """Create instance from dictionary"""

tests/enrich_hf_validation/evaluation_engine.py

@@ -100,7 +100,7 @@ def evaluate_model(
     flagged issues.
     """
     civitai = metadata.get("civitai") or {}
-    trained_words: List[str] = civitai.get("trainedWords") or metadata.get("trainedWords") or []
+    trained_words: List[str] = civitai.get("trainedWords") or []
     short_desc: str = civitai.get("description") or ""
     tags: List[str] = metadata.get("tags") or []
     notes: str = metadata.get("notes") or ""

tests/enrich_hf_validation/metadata_constructor.py

@@ -149,7 +149,6 @@ def create_initial_metadata(
         "metadata_source": "",
         "last_checked_at": 0,
         "hash_status": "completed",
-        "trainedWords": [],
         "hf_url": hf_url,
         "usage_tips": "{}",
     }

tests/services/test_post_processor.py

@@ -164,7 +164,7 @@ class TestEnrichHfMetadata:
                 skill_name="enrich_hf_metadata",
                 model_path="/p.safetensors",
                 llm_output=llm,
-                metadata={"trainedWords": []},
+                metadata={},
             )
         applied = mock_apply.call_args[0][1]
         assert applied["civitai"]["trainedWords"] == ["trigger1", "trigger2"]