feat(agent): improve enrich_hf_metadata skill with priority_tags, preview_url fix, civitai.trainedWords

- Add identify_model_type() helper to determine lora/checkpoint/embedding
- Pass priority_tags from user settings to LLM prompt for tag relevance
- SKILL.md: instruct LLM to exclude technical/generic HF tags, cross-reference
  against priority_tags; forbid ['None'] placeholder for trigger words
- post_processor: fix preview_url not updated after download (now writes local
  .webp path to metadata); write trigger words to civitai.trainedWords instead
  of top-level; sanitize ['None']/'null'/'n/a' placeholder values to []
- download_preview() now returns str | None (local path) instead of bool
- Update tests for new return type and nested civitai.trainedWords structure

py/services/agent/agent_service.py

@@ -334,10 +334,11 @@ class AgentService:
         """Gather variables for the skill's prompt template.
 
         Reads metadata, fetches the HF README (if applicable), lists available
-        base models, and returns a dict that maps to ``{{variable}}``
-        placeholders in ``prompt.md``.
+        base models, loads user priority tags, and returns a dict that maps to
+        ``{{variable}}`` placeholders in ``prompt.md``.
         """
-        from ...agent_cli import list_base_models
+        from ...agent_cli import identify_model_type, list_base_models
+        from ..settings_manager import SettingsManager
 
         context: Dict[str, Any] = {
             "model_path": model_path,
@@ -346,6 +347,7 @@ class AgentService:
             "readme_content": "",
             "current_metadata": {},
             "base_models": [],
+            "priority_tags": "",
         }
 
         context["current_metadata"] = {
@@ -371,6 +373,18 @@ class AgentService:
         except Exception as exc:
             logger.debug("Failed to list base models: %s", exc)
 
+        # Determine model type and load the corresponding priority_tags
+        try:
+            model_type = await identify_model_type(model_path)
+            context["model_type"] = model_type
+            settings = SettingsManager()
+            priority_config = settings.get_priority_tag_config()
+            context["priority_tags"] = priority_config.get(model_type, "")
+        except Exception as exc:
+            logger.debug("Failed to load priority tags: %s", exc)
+            context["model_type"] = "lora"
+            context["priority_tags"] = ""
+
         return context
 
     @staticmethod

py/services/agent/post_processor.py

@@ -86,14 +86,16 @@ class PostProcessor:
         if new_base and self._should_overwrite(current_base, is_hf_model):
             updates["base_model"] = new_base
 
-        # trainedWords / trigger words
         new_triggers = llm_output.get("trigger_words", [])
         if isinstance(new_triggers, list):
             cleaned = [t.strip() for t in new_triggers if t.strip()]
-            if cleaned:
-                current_triggers = metadata.get("trainedWords") or []
-                if self._should_overwrite_list(current_triggers, is_hf_model):
-                    updates["trainedWords"] = cleaned
+            cleaned = [t for t in cleaned if t.lower() not in ("none", "null", "n/a")]
+            current_civitai = metadata.get("civitai") or {}
+            current_triggers = current_civitai.get("trainedWords") or []
+            if self._should_overwrite_list(current_triggers, is_hf_model):
+                civitai_updates = dict(current_civitai)
+                civitai_updates["trainedWords"] = cleaned
+                updates["civitai"] = civitai_updates
 
         # modelDescription
         new_desc = (llm_output.get("description") or "").strip()
@@ -102,7 +104,7 @@ class PostProcessor:
             if self._should_overwrite(current_desc, is_hf_model):
                 updates["modelDescription"] = new_desc
 
-        # tags — merge with existing, deduplicate (case-insensitive)
+        # tags
         new_tags = llm_output.get("tags", [])
         if isinstance(new_tags, list) and new_tags:
             existing_tags = metadata.get("tags") or []
@@ -114,16 +116,17 @@ class PostProcessor:
         updates["metadata_source"] = "agent:enrich_hf_metadata"
         updates["llm_enriched_at"] = datetime.now(timezone.utc).isoformat()
 
-        # -- Persist updates ------------------------------------------------
+        preview_remote_url = (llm_output.get("preview_url") or "").strip()
+        current_preview = metadata.get("preview_url") or ""
+        if preview_remote_url and not (current_preview and os.path.exists(current_preview)):
+            local_path = await download_preview(model_path, preview_remote_url)
+            if local_path:
+                preview_downloaded = True
+                updates["preview_url"] = local_path
+
         if updates:
             updated_fields = await apply_metadata_updates(model_path, updates)
 
-        # -- Download preview -----------------------------------------------
-        preview_url = (llm_output.get("preview_url") or "").strip()
-        current_preview = metadata.get("preview_url") or ""
-        if preview_url and not (current_preview and os.path.exists(current_preview)):
-            preview_downloaded = await download_preview(model_path, preview_url)
-
         # -- Refresh scanner cache ------------------------------------------
         if updated_fields or preview_downloaded:
             await refresh_cache(model_path)

py/services/agent/skills/enrich_hf_metadata/SKILL.md

@@ -21,6 +21,16 @@ You are an expert assistant for AI image generation models. Your task is to extr
 {{current_metadata}}
 ```
 
+## User Priority Tags Reference
+
+The user has configured the following list of **meaningful tag categories** for this model type (`{{model_type}}`):
+
+```
+{{priority_tags}}
+```
+
+These are the subjects, styles, and concepts the user considers useful for categorization. Use this list as a **reference** when evaluating tags (see the **tags** section below).
+
 ## Available Base Models
 
 The following base models are currently valid in this system:
@@ -37,7 +47,7 @@ The following base models are currently valid in this system:
 Extract the following information from the README content above:
 
 ### base_model
-The base model this LoRA/checkpoint was trained on. Use EXACTLY one of the names from the **Available Base Models** list above. Do not invent new names or use aliases.
+The base model this model was trained on. Use EXACTLY one of the names from the **Available Base Models** list above. Do not invent new names or use aliases.
 
 Check the YAML frontmatter (between --- markers) for `base_model:` first, then look at the description text and safetensors metadata. If you cannot determine it, return an empty string.
 
@@ -46,17 +56,27 @@ The trigger words or activation prompts needed to use this LoRA. Look for:
 - `instance_prompt:` in the YAML frontmatter
 - Phrases like "trigger word:", "trigger:", "use this prompt:", "activation prompt:"
 - Example prompts at the start (usually the first word or phrase before any description)
-Return as an array of strings. If none found, return an empty array.
+Return as an array of strings. If none found, return an empty array `[]`. **Never** return `["None"]` or any placeholder value — a truly empty list means no trigger words exist.
 
 ### description
 A concise 1-2 sentence summary of what this model does. Extract from the "Model description" section or the first paragraph. Return empty string if the README is too minimal.
 
 ### tags
-3-8 relevant tags for categorizing this model. Extract from:
-- The YAML frontmatter `tags:` list (often contains excellent categorization tags)
-- The model type (e.g. "lora", "checkpoint", "flux", "sdxl")
-- The style/subject (e.g. "anime", "photorealistic", "style", "character")
-All lowercase, no spaces. Return empty array if none found.
+3-8 relevant tags for categorizing this model. **Quality over quantity.**
+
+Sources to consider:
+- The YAML frontmatter `tags:` list
+- The subject, style, character, or concept the model represents
+
+**Critical filtering rules — apply them strictly:**
+
+1. **Exclude technical/generic tags.** Reject any tag that describes the model's **training methodology, framework, architecture, or modality** rather than its content. Examples to exclude: `text-to-image`, `diffusers`, `lora`, `dreambooth`, `diffusers-training`, `flux`, `sdxl`, `checkpoint`, `pytorch`, `safetensors`, `fine-tuning`, `stable-diffusion`, and any variant of these.
+
+2. **Cross-reference against the priority_tags reference.** Only include a tag if it meaningfully describes what the model actually creates (subject, style, character type) and is semantically close to one of the priority_tags. If none of the README's tags match meaningful categories, prefer returning a smaller set or an empty array over including low-value tags.
+
+3. **All lowercase, no spaces, no hyphens** (use single words like `"photorealistic"`, `"anime"`, `"character"`).
+
+Return empty array if no meaningful content tags remain after filtering.
 
 ### preview_url
 The URL of the most suitable preview image from the README. Look for image tags (e.g. `![alt](url)`) and the YAML frontmatter `widget:` section (which often has `output.url` fields). Choose the first image that appears to be a generation example (not a logo or diagram). Construct the absolute URL as `https://huggingface.co/{{repo}}/resolve/main/{filename}`. If no suitable image is found, return an empty string.
@@ -87,3 +107,4 @@ Important:
 - Only include the JSON object, no other text
 - If a field cannot be determined, use an empty string or empty array
 - Do not fabricate information not supported by the README
+- Never use placeholder values like `"None"` or `"unknown"` for missing data — use empty string or empty array