feat(modelscope): read the model-detail API for card extras

ModelScope's model card is not just README.md: the author's summary
(Description), the site-curated tags (OfficialTags), the internal
architecture enums (VisionFoundation/SubVisionFoundation) and — per
published version — the model filenames with that file's example images
(coverImages) and trigger words all live in the model-detail API.
AIGC repositories there frequently ship an auto-generated boilerplate
README and put the only useful text in Description, so reading just the
README yielded almost nothing.

Add `ModelSource.fetch_model_card_context()` returning a new
`ModelCardContext`, implemented by ModelScopeSource against the public
(no API key) detail endpoint. Example images are matched to the model's
basename through each version's `stats.fileList`, so every checkpoint in
a collection repository gets its own images rather than a sibling's.

Consume the context in the post-processor:

* example images seed `civitai.images` and, being per-file, take priority
  in the preview fallback chain
* the author summary becomes a paragraph in `modelDescription` and fills
  `civitai.description` when the LLM returns no short description
* site-curated tags are always merged in, which also fixes the official
  `character-enhancement` being dropped by the prompt's no-hyphen rule
* per-file trigger words are used before the repo-wide YAML
  `instance_prompt`
* an explicitly stated strength range is recovered by regex so
  `usage_tips` is populated even without an LLM

The prompt gains a Site-Provided Metadata section so the LLM can prefer
the site's first-hand data over its own guesses.

py/services/agent/post_processor.py

@@ -10,12 +10,16 @@ refresh cache).  All actual I/O is delegated to :mod:`~py.metadata_ops`.
 
 from __future__ import annotations
 
+import html
 import json
 import logging
 import os
 import re
 from datetime import datetime, timezone
-from typing import Any, Dict, List, Optional
+from typing import TYPE_CHECKING, Any, Dict, List, Optional
+
+if TYPE_CHECKING:  # pragma: no cover - typing only
+    from ..model_sources import ModelCardContext
 
 logger = logging.getLogger(__name__)
 
@@ -42,6 +46,8 @@ class PostProcessor:
         llm_output: Dict[str, Any],
         metadata: Dict[str, Any],
         readme_content: str = "",
+        source_context: Optional["ModelCardContext"] = None,
+        resolved_base_model: str = "",
     ) -> Dict[str, Any]:
         """Route *llm_output* to the correct skill post-processor.
 
@@ -49,12 +55,21 @@ class PostProcessor:
         that is converted to HTML and stored as ``modelDescription`` for
         the description tab.
 
+        *source_context* carries the extras the model site publishes outside
+        the README (author description, per-file example images, trigger
+        words).  It is ``None`` for callers that have none.
+
+        *resolved_base_model* is the canonical base-model name the site's own
+        hints resolve to, used when the LLM did not supply one (which is the
+        normal case when the LLM was skipped).
+
         Returns a dict with keys ``success`` (bool), ``updated_fields`` (list),
         ``preview_downloaded`` (bool), and ``errors`` (list).
         """
         if skill_name == "enrich_hf_metadata":
             return await self._process_enrich_hf_metadata(
-                model_path, llm_output, metadata, readme_content,
+                model_path, llm_output, metadata, readme_content, source_context,
+                resolved_base_model,
             )
         return {
             "success": False,
@@ -72,6 +87,8 @@ class PostProcessor:
         llm_output: Dict[str, Any],
         metadata: Dict[str, Any],
         readme_content: str = "",
+        source_context: Optional["ModelCardContext"] = None,
+        resolved_base_model: str = "",
     ) -> Dict[str, Any]:
         from ...metadata_ops import (
             apply_metadata_updates,
@@ -109,8 +126,11 @@ class PostProcessor:
         # -- Collect updates -----------------------------------------------
         updates: Dict[str, Any] = {}
 
-        # base_model
+        # base_model — the LLM's mapping wins; when it returned nothing usable,
+        # fall back to the canonical name the site's own hints resolve to.
         new_base = (llm_output.get("base_model") or "").strip()
+        if not new_base:
+            new_base = (resolved_base_model or "").strip()
         current_base = metadata.get("base_model", "") or ""
         if new_base and self._should_overwrite(current_base, is_source_model):
             updates["base_model"] = new_base
@@ -131,14 +151,29 @@ class PostProcessor:
                 trig_civitai["trainedWords"] = cleaned
                 updates["civitai"] = trig_civitai
 
-        # modelDescription — from raw README content (converted to HTML)
+        # modelDescription — the author's own summary (when the site keeps one
-        if readme_content and is_source_model:
+        # outside the README, e.g. ModelScope's ``Description``) followed by the
+        # README converted to HTML.
+        site_description = (
+            (source_context.description if source_context else "") or ""
+        ).strip()
+        if is_source_model and (site_description or readme_content):
+            parts: List[str] = []
+            if site_description:
+                parts.append(f"<p>{html.escape(site_description)}</p>")
+            if readme_content:
                 converted = convert_readme_to_html(readme_content)
                 if converted:
-                updates["modelDescription"] = converted
+                    parts.append(converted)
+            if parts:
+                updates["modelDescription"] = "\n".join(parts)
 
-        # short_description → civitai.description (for "About this version")
+        # short_description → civitai.description (for "About this version").
+        # Falls back to the site's author summary, which for ModelScope AIGC
+        # models is frequently the only human-written text available.
         short_desc = (llm_output.get("short_description") or "").strip()
+        if not short_desc:
+            short_desc = site_description
         if short_desc and is_source_model:
             current_civitai = metadata.get("civitai") or {}
             desc_civitai = dict(current_civitai)
@@ -147,19 +182,31 @@ class PostProcessor:
             desc_civitai["description"] = short_desc
             updates["civitai"] = desc_civitai
 
-        # gallery images → civitai.images (from YAML frontmatter widget entries
+        # gallery images → civitai.images (site example images, YAML frontmatter
-        # and Sample Gallery markdown tables in the README body)
+        # widget entries, and Sample Gallery markdown tables in the README body)
-        gallery_images: List[Dict[str, Any]] = []
+        rec_width = llm_output.get("recommended_width") or 0
-        if readme_content and is_source_model:
+        rec_height = llm_output.get("recommended_height") or 0
-            repo = source_id
-            if repo:
-                rec_w = llm_output.get("recommended_width") or 0
-                rec_h = llm_output.get("recommended_height") or 0
 
+        # Example images the site publishes for *this* file.  They are matched
+        # by filename, so they are the most precise preview source available
+        # and the only one for repositories whose README carries no images.
+        site_images: List[Dict[str, Any]] = []
+        if is_source_model and source_context is not None:
+            site_images = [
+                _example_image(url, rec_width, rec_height)
+                for url in source_context.example_images
+                if url
+            ]
+
+        gallery_images: List[Dict[str, Any]] = []
+        if (readme_content or site_images) and is_source_model:
+            repo = source_id
+            readme_images: List[Dict[str, Any]] = []
+            if readme_content and repo:
                 # 1. Widget images (YAML frontmatter)
                 gallery = extract_gallery_images(
                     readme_content, repo,
-                    default_width=rec_w, default_height=rec_h,
+                    default_width=rec_width, default_height=rec_height,
                     base_url=asset_base_url,
                 )
 
@@ -168,7 +215,7 @@ class PostProcessor:
                 table_images = extract_gallery_table_images(
                     readme_content, repo,
                     existing_urls=existing_urls,
-                    default_width=rec_w, default_height=rec_h,
+                    default_width=rec_width, default_height=rec_height,
                     base_url=asset_base_url,
                 )
                 existing_urls.update(img["url"] for img in table_images if img.get("url"))
@@ -177,7 +224,7 @@ class PostProcessor:
                 simple_images = extract_simple_markdown_images(
                     readme_content, repo,
                     existing_urls=existing_urls,
-                    default_width=rec_w, default_height=rec_h,
+                    default_width=rec_width, default_height=rec_height,
                     base_url=asset_base_url,
                 )
                 existing_urls.update(img["url"] for img in simple_images if img.get("url"))
@@ -186,11 +233,15 @@ class PostProcessor:
                 html_images = extract_html_img_tags(
                     readme_content, repo,
                     existing_urls=existing_urls,
-                    default_width=rec_w, default_height=rec_h,
+                    default_width=rec_width, default_height=rec_height,
                     base_url=asset_base_url,
                 )
 
-                all_images = gallery + table_images + simple_images + html_images
+                readme_images = gallery + table_images + simple_images + html_images
+
+            # Site images come first so the preview fallback below prefers an
+            # image that is known to belong to this exact file.
+            all_images = _dedupe_images(site_images + readme_images)
             if all_images:
                 gallery_images = all_images
                 current_civitai = metadata.get("civitai") or {}
@@ -200,11 +251,21 @@ class PostProcessor:
                 gallery_civitai["images"] = all_images
                 updates["civitai"] = gallery_civitai
 
-        # tags
+        # tags — the site's curated tags are authoritative content vocabulary, so
+        # they are kept alongside whatever the LLM proposed (the LLM is skipped
+        # entirely when the site data is complete, which is why this cannot rely
+        # on ``llm_output`` alone).
         new_tags = llm_output.get("tags", [])
-        if isinstance(new_tags, list) and new_tags:
+        candidate_tags: List[str] = []
+        if is_source_model and source_context is not None:
+            candidate_tags.extend(source_context.official_tags)
+        if isinstance(new_tags, list):
+            candidate_tags.extend(
+                tag for tag in new_tags if tag not in candidate_tags
+            )
+        if candidate_tags:
             existing_tags = metadata.get("tags") or []
-            merged = self._merge_tags(existing_tags, new_tags)
+            merged = self._merge_tags(existing_tags, candidate_tags)
             if len(merged) > len(existing_tags) or is_source_model:
                 updates["tags"] = merged
 
@@ -217,16 +278,22 @@ class PostProcessor:
         if raw_confidence:
             updates["_llm_confidence"] = raw_confidence
 
-        # Fallback: extract instance_prompt from YAML frontmatter when the LLM
+        # Fallback: use the trigger words the site records for this exact file,
-        # returned empty trigger words but the README has instance_prompt.
+        # then the README's YAML `instance_prompt`, when the LLM returned none.
         if trigger_words_empty:
+            site_triggers = (
+                list(source_context.trigger_words) if source_context else []
+            )
+            if not site_triggers:
                 instance_prompt = _extract_yaml_instance_prompt(readme_content)
                 if instance_prompt:
+                    site_triggers = [instance_prompt]
+            if site_triggers:
                 current_civitai = metadata.get("civitai") or {}
                 trig_civitai = dict(current_civitai)
                 if "civitai" in updates and isinstance(updates["civitai"], dict):
                     trig_civitai.update(updates["civitai"])
-                trig_civitai["trainedWords"] = [instance_prompt]
+                trig_civitai["trainedWords"] = site_triggers
                 updates["civitai"] = trig_civitai
 
         preview_remote_url = (llm_output.get("preview_url") or "").strip()
@@ -260,8 +327,12 @@ class PostProcessor:
         if new_notes:
             updates["notes"] = new_notes
 
-        # usage_tips — JSON string (e.g. {"strength_min":0.85,"strength_max":1.4})
+        # usage_tips — JSON string (e.g. {"strength_min":0.85,"strength_max":1.4}).
+        # When the LLM returned nothing, recover an explicitly stated strength
+        # range from the author summary so the value is not lost.
         raw_tips = (llm_output.get("usage_tips") or "").strip()
+        if not raw_tips or raw_tips == "{}":
+            raw_tips = _extract_usage_tips(site_description)
         if raw_tips and raw_tips != "{}":
             try:
                 json.loads(raw_tips)
@@ -324,6 +395,129 @@ class PostProcessor:
 # ------------------------------------------------------------------
 
 
+#: Separator between a label and its value.  Published model cards routinely
+#: wrap the numbers in markdown emphasis or quotes (``strength: **0.85 - 1.4**``,
+#: ``CLIP 强度「0.5」``), so those are absorbed rather than treated as a break.
+_EMPHASIS = "[\"'\u201c\u201d\u300c\u300d*_`\\s]*"
+
+#: An explicitly stated strength/weight range, e.g. ``权重0.5-1.2``,
+#: ``强度 0.8 ~ 1.2``, ``strength: **0.85 - 1.4**``.
+_RANGE_DASH = "(?:-|\u2010|\u2011|\u2012|\u2013|\u2014|\uff0d|~|\uff5e|\u81f3|\u5230|to)"
+
+_STRENGTH_RANGE_RE = re.compile(
+    "(?:\u6743\u91cd|\u5f3a\u5ea6|strength|weight)" + _EMPHASIS + "[:\uff1a]?" + _EMPHASIS
+    + r"(\d+(?:\.\d+)?)" + _EMPHASIS + _RANGE_DASH + _EMPHASIS
+    + r"(\d+(?:\.\d+)?)",
+    re.IGNORECASE,
+)
+
+#: A single strength/weight value, e.g. ``strength: 0.6``, ``权重 0.8``.
+_STRENGTH_VALUE_RE = re.compile(
+    "(?:\u6743\u91cd|\u5f3a\u5ea6|strength|weight)" + _EMPHASIS + "[:\uff1a]?" + _EMPHASIS
+    + r"(\d+(?:\.\d+)?)",
+    re.IGNORECASE,
+)
+
+#: ``clip strength: 0.5`` / ``CLIP 强度 0.5``.
+_CLIP_STRENGTH_RE = re.compile(
+    "clip" + _EMPHASIS + "(?:\u5f3a\u5ea6|strength)" + _EMPHASIS + "[:\uff1a]?" + _EMPHASIS
+    + r"(\d+(?:\.\d+)?)",
+    re.IGNORECASE,
+)
+
+#: ``clip skip: 2`` / ``CLIP 跳过 2``.
+_CLIP_SKIP_RE = re.compile(
+    "clip" + _EMPHASIS + "(?:skip|\u8df3\u8fc7)" + _EMPHASIS + "[:\uff1a]?" + _EMPHASIS
+    + r"(\d+)",
+    re.IGNORECASE,
+)
+
+
+def _extract_usage_tips(text: str) -> str:
+    """Extract stated strength/CLIP recommendations from prose.
+
+    This is the deterministic counterpart to the LLM's ``usage_tips`` output,
+    used when the LLM was skipped.  It only recognises explicitly written
+    values — it never infers a range — and returns ``""`` when it finds none.
+
+    Returns:
+        A JSON string matching the skill's ``usage_tips`` schema, or ``""``.
+    """
+
+    if not text:
+        return ""
+
+    tips: Dict[str, Any] = {}
+
+    # CLIP strength is resolved first and then blanked out, so the generic
+    # strength patterns cannot mistake `CLIP 强度 0.5` for the LoRA strength.
+    text_for_strength = text
+    clip_strength = _CLIP_STRENGTH_RE.search(text_for_strength)
+    if clip_strength:
+        tips["clip_strength"] = float(clip_strength.group(1))
+        text_for_strength = (
+            text_for_strength[: clip_strength.start()]
+            + " "
+            + text_for_strength[clip_strength.end() :]
+        )
+
+    range_match = _STRENGTH_RANGE_RE.search(text_for_strength)
+    if range_match:
+        low = float(range_match.group(1))
+        high = float(range_match.group(2))
+        if low > high:
+            low, high = high, low
+        tips["strength_min"] = low
+        tips["strength_max"] = high
+        tips["strength_range"] = f"{low:g}-{high:g}"
+    else:
+        value_match = _STRENGTH_VALUE_RE.search(text_for_strength)
+        if value_match:
+            tips["strength"] = float(value_match.group(1))
+
+    clip_skip = _CLIP_SKIP_RE.search(text)
+    if clip_skip:
+        tips["clip_skip"] = int(clip_skip.group(1))
+
+    if not tips:
+        return ""
+    return json.dumps(tips, ensure_ascii=False)
+
+
+def _example_image(url: str, width: int, height: int) -> Dict[str, Any]:
+    """Build a ``civitai.images`` entry for a site-provided example image.
+
+    The site publishes no prompt alongside these images, so the entry carries
+    empty prompt metadata and the LLM's recommended dimensions when it found
+    any (falling back to the same 512px placeholder the README extractors use).
+    """
+
+    return {
+        "url": url,
+        "type": "image",
+        "nsfwLevel": 0,
+        "width": width or 512,
+        "height": height or 512,
+        "meta": {"prompt": "", "negativePrompt": ""},
+        "hasMeta": False,
+        "hasPositivePrompt": False,
+    }
+
+
+def _dedupe_images(images: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    """Drop later entries that repeat an earlier image URL, keeping order."""
+
+    seen: set[str] = set()
+    unique: List[Dict[str, Any]] = []
+    for image in images:
+        url = image.get("url") or ""
+        if not url or url in seen:
+            continue
+        seen.add(url)
+        unique.append(image)
+    return unique
+
+
 def _extract_yaml_instance_prompt(readme_content: str) -> str:
     """Extract ``instance_prompt`` from the YAML frontmatter of a HF README.
 

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

@@ -25,6 +25,34 @@ You are an expert assistant for AI image generation models. Your task is to extr
 {{current_metadata}}
 ```
 
+## Site-Provided Metadata (any field may be empty)
+
+The model site publishes the following **alongside** the README. It is
+first-hand information recorded by the site itself, so it outranks anything
+you would otherwise guess:
+
+- **Author description**: {{source_description}}
+- **Base model reported by the site**: {{source_base_model}}
+- **Trigger words recorded for this file**: {{source_trigger_words}}
+- **Site-curated tags**:
+{{source_official_tags}}
+- **Example image URLs for this file**:
+{{source_example_images}}
+
+Use it as follows:
+
+- A weight or strength range stated in the **author description** belongs in
+  ``usage_tips`` (and in ``notes``); do not leave ``usage_tips`` empty when the
+  description states one.
+- When the author description exists, base ``short_description`` on it rather
+  than on the README, which on some sites is auto-generated boilerplate.
+- Treat the **site-curated tags** as strong signals for ``tags``: they are
+  already a curated content vocabulary, so prefer them over invented words.
+- Treat the **base model reported by the site** as a strong hint for
+  ``base_model``, but still map it to the EXACT canonical name from the
+  available base-model list.
+- Use the **example image URLs** when the README contains no usable image.
+
 ## User Priority Tags Reference
 
 The user has configured the following list of **meaningful tag categories** for this model type (`{{model_type}}`):
@@ -55,10 +83,11 @@ Extract the following information from the README content above:
 ### base_model
 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 for ``base_model:`` first. If the frontmatter has no ``base_model:``, look at the **model filename** (``{{model_basename}}``), YAML ``tags:``, README title and first paragraph for clues — the base model family is often embedded in the name
+Check the **base model reported by the site** (above) and the YAML frontmatter ``base_model:`` first. If neither yields a match, look at the **model filename** (``{{model_basename}}``), YAML ``tags:``, README title and first paragraph for clues — the base model family is often embedded in the name
 
 ### trigger_words
 The trigger words or activation prompts needed to use this LoRA. Look for:
+- The **trigger words recorded for this file** in the site-provided metadata (most authoritative)
 - `instance_prompt:` in the YAML frontmatter
 - Phrases like "trigger word:", "trigger:", "use this prompt:", "activation prompt:"
 - In collection repos: the trigger section **specific to this model file** (look near matching download links or anchor IDs)
@@ -66,12 +95,13 @@ The trigger words or activation prompts needed to use this LoRA. Look for:
 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.
 
 ### short_description
-A concise 1-2 sentence summary of what this model does. Extract from the "Model description" section or the first paragraph.  For collection repos, focus on the **specific model version** matching `{{model_basename}}`, not the repo as a whole.  Return empty string if the README is too minimal.
+A concise 1-2 sentence summary of what this model does. For collection repos, focus on the **specific model version** matching `{{model_basename}}`, not the repo as a whole.  Prefer the **author description** from the site-provided metadata when it is present; otherwise extract from the "Model description" section or the first paragraph.  Return empty string if the available content is too minimal.
 
 ### tags
 3-8 relevant tags for categorizing this model. **Quality over quantity.**
 
 Sources to consider:
+- The **site-curated tags** from the site-provided metadata (these are already filtered content tags — prefer them)
 - The YAML frontmatter `tags:` list (filter out technical ones — see below)
 - The subject, style, character, or concept the model represents
 - The model filename itself may give clues (e.g. "pokemon", "anime", "pixelart")
@@ -82,7 +112,9 @@ Sources to consider:
 
 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"`).
+3. **All lowercase, no spaces, no hyphens** (use single words like `"photorealistic"`, `"anime"`, `"character"`). This rule applies to Latin-script tags; when the model's own tags are in another script (e.g. Chinese), keep them verbatim instead of dropping or translating them.
+
+4. **Never invent a tag** that neither the site-provided metadata, the YAML frontmatter, nor the README text supports.
 
 Return empty array if no meaningful content tags remain after filtering.
 
@@ -95,13 +127,13 @@ The URL of the most suitable preview image from the README. Look for:
 - The YAML frontmatter `widget:` section (which often has `output.url` fields)
 - In collection repos: the sample images listed **under the section** for this specific model version
 - Generic `![alt](url)` in the body
-Choose the first image that appears to be a generation example (not a logo or diagram). Construct the absolute URL from the repository raw-file base URL (`{{asset_base_url}}`) plus the relative path. If no suitable image is found, return an empty string.
+Choose the first image that appears to be a generation example (not a logo or diagram). Construct the absolute URL from the repository raw-file base URL (`{{asset_base_url}}`) plus the relative path. If the README has no suitable image, fall back to the site-provided **example image URLs** for this file. If nothing is available, return an empty string.
 
 ### notes
-A plain-text summary of the model card's key practical usage information. Combine trigger words, style modifiers, recommended parameters (steps, CFG, resolution, sampler), and any setup tips into a readable paragraph.  For collection repos, focus on the **specific model version** matching `{{model_basename}}`.  Return empty string if the README has no useful usage info.
+A plain-text summary of the model card's key practical usage information. Combine trigger words, style modifiers, recommended parameters (steps, CFG, resolution, sampler), and any setup tips into a readable paragraph.  For collection repos, focus on the **specific model version** matching `{{model_basename}}`.  Include the **author description** from the site-provided metadata when it is present.  Return empty string if there is no useful usage info.
 
 ### usage_tips
-A JSON string with structured usage recommendations. Extract from the README any explicit ranges or recommended values (e.g. "Set LoRA strength: **0.85 - 1.4**", "CLIP strength: 0.5"). Possible fields (include only those you can determine):
+A JSON string with structured usage recommendations. Extract from the **author description** (site-provided metadata) and the README any explicit ranges or recommended values (e.g. "Set LoRA strength: **0.85 - 1.4**", "CLIP strength: 0.5", "权重0.5-1.2"). Possible fields (include only those you can determine):
 
 ```json
 {

py/services/model_sources/__init__.py

@@ -11,6 +11,7 @@ from __future__ import annotations
 from .base import (
     GROUP_PREFIXES,
     HTTP_TIMEOUT,
+    ModelCardContext,
     ModelSource,
     ModelSourceError,
     SourceRef,
@@ -45,6 +46,7 @@ __all__ = [
     "GROUP_PREFIXES",
     "HTTP_TIMEOUT",
     "LEGACY_HF_URL_FIELD",
+    "ModelCardContext",
     "ModelSource",
     "ModelSourceError",
     "HuggingFaceSource",

py/services/model_sources/base.py

@@ -8,6 +8,8 @@ know about such a site is expressed by :class:`ModelSource`:
 * how to recognise one of its URLs (:meth:`ModelSource.parse`)
 * the canonical page URL for a source id (:meth:`ModelSource.canonical_url`)
 * how to fetch the model card (:meth:`ModelSource.fetch_model_card`)
+* how to fetch the extras that live *outside* the README
+  (:meth:`ModelSource.fetch_model_card_context`)
 * how to turn repository-relative asset paths into absolute URLs
   (:meth:`ModelSource.asset_base_url`)
 * which capabilities the site actually supports
@@ -22,7 +24,7 @@ from __future__ import annotations
 import logging
 import os
 import re
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 from typing import Any, Iterable, Optional
 
 import aiohttp
@@ -61,6 +63,56 @@ class SourceRef:
     """Canonical URL of the model page."""
 
 
+@dataclass
+class ModelCardContext:
+    """Site-specific extras that accompany a model's README model card.
+
+    A model card is not always just ``README.md``.  ModelScope, for example,
+    keeps the author's summary, the site-curated tags, and the per-file
+    example images in its model-detail API rather than in the repository.
+    Sources with no such extras return an empty context (the default), so
+    every field here must be treated as optional by callers.
+    """
+
+    description: str = ""
+    """Author-written summary shown on the model page, outside the README."""
+
+    base_model: str = ""
+    """Base model as reported by the site (possibly a site-local id)."""
+
+    base_model_aliases: list[str] = field(default_factory=list)
+    """Other names the site uses for the same base model.
+
+    Sites often publish both a link-style id (``krea/Krea-2-Turbo``) and an
+    internal architecture enum (``KREA_2``).  The enum usually normalises
+    cleanly onto this system's canonical vocabulary, so it is the better
+    resolution hint for :mod:`py.services.agent.base_model_resolver`.
+    """
+
+    official_tags: list[str] = field(default_factory=list)
+    """Content tags curated by the site itself."""
+
+    example_images: list[str] = field(default_factory=list)
+    """Absolute URLs of example images for the requested model file."""
+
+    trigger_words: list[str] = field(default_factory=list)
+    """Trigger words the site records for the requested model file."""
+
+    def is_empty(self) -> bool:
+        """Return ``True`` when the site contributed nothing extra."""
+
+        return not any(
+            (
+                self.description,
+                self.base_model,
+                self.base_model_aliases,
+                self.official_tags,
+                self.example_images,
+                self.trigger_words,
+            )
+        )
+
+
 class ModelSourceError(Exception):
     """Raised when a model source cannot satisfy a request.
 
@@ -230,6 +282,22 @@ class ModelSource:
 
         return ""
 
+    async def fetch_model_card_context(
+        self, source_id: str, filename: str = ""
+    ) -> ModelCardContext:
+        """Return the card extras the site keeps outside the README.
+
+        *filename* is the model file's basename (no directory) and selects
+        the right entry when a repository holds several models.  Sites whose
+        model card is fully described by :meth:`fetch_model_card` need no
+        override and inherit this empty context.
+
+        Implementations must never raise: enrichment treats a missing
+        context as "the site had nothing extra to say".
+        """
+
+        return ModelCardContext()
+
     # ------------------------------------------------------------------
     # Download support
     # ------------------------------------------------------------------
@@ -301,6 +369,7 @@ def filter_weight_files(entries: Iterable[tuple[str, int]]) -> list[dict[str, An
 __all__ = [
     "GROUP_PREFIXES",
     "HTTP_TIMEOUT",
+    "ModelCardContext",
     "ModelSource",
     "ModelSourceError",
     "SourceRef",

py/services/model_sources/modelscope.py

@@ -2,13 +2,19 @@
 
 ModelScope exposes the same "model card as README.md" convention as
 Hugging Face, including a YAML frontmatter block that often carries
-``base_model:`` and ``trigger_words:``.  Three public endpoints are used,
+``base_model:`` and ``trigger_words:``.  Four public endpoints are used,
 none of which requires an API key for public models:
 
 * ``/models/{owner}/{name}/resolve/{revision}/README.md`` — raw model card
 * ``/api/v1/models/{owner}/{name}/repo?Revision=..&FilePath=README.md`` —
   the same content through the API, used as a fallback when the resolve
   URL is unavailable.
+* ``/api/v1/models/{owner}/{name}`` — the model-detail payload behind the
+  model page.  It carries the author's summary (``Description``), the
+  site-curated tags (``OfficialTags``), and, per published version, the
+  model filenames (``MuseInfo.versions[].stats.fileList``) together with
+  that file's example images (``coverImages``) and trigger words.  See
+  :meth:`ModelScopeSource.fetch_model_card_context`.
 * ``/api/v1/models/{owner}/{name}/repo/files?Revision=..`` — the file
   listing backing the download picker.  It reports real sizes for LFS
   files (not the pointer size), so no extra HEAD request is needed.
@@ -22,10 +28,14 @@ valid; the CDN URL must never be cached.
 
 from __future__ import annotations
 
+import json
 import logging
+import os
 import re
+from typing import Any
 
 from .base import (
+    ModelCardContext,
     ModelSource,
     ModelSourceError,
     fetch_json,
@@ -95,6 +105,47 @@ class ModelScopeSource(ModelSource):
                 return text
         return ""
 
+    async def fetch_model_card_context(
+        self, source_id: str, filename: str = ""
+    ) -> ModelCardContext:
+        """Read the model-detail API that backs the ModelScope model page.
+
+        ModelScope splits a model card in two: ``README.md`` holds the
+        long-form content, while the author's summary, the site-curated tags,
+        and the per-file example images live only here.  AIGC repositories
+        frequently ship an auto-generated README ("the contributor provided
+        no further description") and put everything useful in ``Description``,
+        so enrichment that reads only the README comes back nearly empty.
+
+        Example images are matched to *filename* through each version's
+        ``stats.fileList``, which means the images returned belong to the
+        exact ``.safetensors`` being enriched — essential for collection
+        repositories, where every checkpoint has its own sample image.
+        """
+
+        status, payload = await fetch_json(
+            f"https://modelscope.cn/api/v1/models/{source_id}"
+        )
+        if status != 200 or not isinstance(payload, dict):
+            logger.debug("ModelScope detail API returned HTTP %s for %s", status, source_id)
+            return ModelCardContext()
+        data = payload.get("Data")
+        if not isinstance(data, dict):
+            return ModelCardContext()
+
+        context = ModelCardContext(
+            description=_clean_text(data.get("Description")),
+            base_model=_first_string(data.get("BaseModel")),
+            base_model_aliases=_base_model_aliases(data),
+            official_tags=_official_tags(data.get("OfficialTags")),
+        )
+
+        versions = _matching_versions(data.get("MuseInfo"), filename)
+        if versions:
+            context.example_images = _cover_image_urls(versions)
+            context.trigger_words = _version_trigger_words(versions)
+        return context
+
     async def list_files(
         self, source_id: str, revision: str = ""
     ) -> list[dict]:
@@ -142,3 +193,213 @@ class ModelScopeSource(ModelSource):
 
 
 __all__ = ["ModelScopeSource"]
+
+
+# ---------------------------------------------------------------------------
+# Model-detail API parsing helpers
+# ---------------------------------------------------------------------------
+
+#: Trigger-word values that mean "the author left this blank".
+_EMPTY_TRIGGER_VALUES = frozenset({"none", "null", "n/a"})
+
+
+def _clean_text(value: Any) -> str:
+    """Return a stripped string for *value*, or ``""`` for anything else."""
+
+    return value.strip() if isinstance(value, str) else ""
+
+
+def _first_string(value: Any) -> str:
+    """Return the first non-empty string in a list, or ``""``."""
+
+    if isinstance(value, list):
+        for item in value:
+            text = _clean_text(item)
+            if text:
+                return text
+    return ""
+
+
+def _base_model_aliases(data: dict[str, Any]) -> list[str]:
+    """Return the site's own names for the base model.
+
+    ModelScope publishes a link-style id (``krea/Krea-2-Turbo``) plus its
+    internal architecture enums (``VisionFoundation: KREA_2``,
+    ``SubVisionFoundation: KREA_2_TURBO``).  The enums are the better
+    resolution hint because they normalise onto this system's canonical
+    vocabulary, so they come first; the owner prefix is also stripped from
+    the link-style ids.
+    """
+
+    aliases: list[str] = []
+    for key in ("VisionFoundation", "SubVisionFoundation"):
+        value = _clean_text(data.get(key))
+        if value and value not in aliases:
+            aliases.append(value)
+
+    base_models = data.get("BaseModel")
+    if isinstance(base_models, list):
+        for item in base_models:
+            text = _clean_text(item)
+            leaf = text.rsplit("/", 1)[-1] if text else ""
+            if leaf and leaf not in aliases:
+                aliases.append(leaf)
+    return aliases
+
+
+def _official_tags(value: Any) -> list[str]:
+    """Extract the site-curated tag values from ``OfficialTags``.
+
+    ModelScope's entries are dicts carrying an English ``Tag`` plus a
+    ``ChineseName``; the English value is the curated content vocabulary, so
+    that is the one surfaced here.
+    """
+
+    tags: list[str] = []
+    if not isinstance(value, list):
+        return tags
+    for entry in value:
+        if not isinstance(entry, dict):
+            continue
+        tag = _clean_text(entry.get("Tag"))
+        if tag and tag not in tags:
+            tags.append(tag)
+    return tags
+
+
+def _version_files(version: dict[str, Any]) -> list[str]:
+    """Return the model filenames covered by one ``MuseInfo.versions`` entry.
+
+    The listing normally sits in ``stats.fileList``; some payloads only
+    carry the same field as a JSON-encoded string under
+    ``modelVersion.stats``, so both shapes are accepted.
+    """
+
+    stats = version.get("stats")
+    files = stats.get("fileList") if isinstance(stats, dict) else None
+
+    if not isinstance(files, list):
+        model_version = version.get("modelVersion")
+        raw = model_version.get("stats") if isinstance(model_version, dict) else None
+        if isinstance(raw, str) and raw.strip():
+            try:
+                decoded = json.loads(raw)
+            except (json.JSONDecodeError, TypeError):
+                decoded = None
+            if isinstance(decoded, dict):
+                files = decoded.get("fileList")
+
+    if not isinstance(files, list):
+        return []
+    return [item for item in files if isinstance(item, str) and item]
+
+
+def _version_show_name(version: dict[str, Any]) -> str:
+    """Return the human-facing version label (e.g. ``c1-st1000``)."""
+
+    model_version = version.get("modelVersion")
+    if not isinstance(model_version, dict):
+        return ""
+    return _clean_text(model_version.get("showName")).lower()
+
+
+def _matching_versions(muse_info: Any, filename: str) -> list[dict[str, Any]]:
+    """Return the ``versions`` entries that publish *filename*.
+
+    Matching is by exact basename first, then by the version's ``showName``
+    appearing in the file stem (which absorbs the naming drift ModelScope
+    sometimes applies to uploaded weights).  All matches are returned so a
+    file re-published across several versions contributes all of its
+    example images.  With no *filename* only an unambiguous single-version
+    repository is used, because a per-file image must never be attributed
+    to the wrong file.
+    """
+
+    if not isinstance(muse_info, dict):
+        return []
+    versions = muse_info.get("versions")
+    if not isinstance(versions, list):
+        return []
+    entries = [entry for entry in versions if isinstance(entry, dict)]
+    if not entries:
+        return []
+
+    if not filename:
+        return entries if len(entries) == 1 else []
+
+    target = os.path.basename(filename).strip().lower()
+    if not target:
+        return []
+    stem = os.path.splitext(target)[0]
+
+    exact: list[dict[str, Any]] = []
+    fuzzy: list[dict[str, Any]] = []
+    for version in entries:
+        files = {os.path.basename(path).lower() for path in _version_files(version)}
+        if target in files:
+            exact.append(version)
+            continue
+        show_name = _version_show_name(version)
+        if show_name and show_name in stem:
+            fuzzy.append(version)
+
+    return exact or fuzzy
+
+
+def _cover_image_urls(versions: list[dict[str, Any]]) -> list[str]:
+    """Collect the example-image URLs published by the given versions."""
+
+    urls: list[str] = []
+    for version in versions:
+        covers = version.get("coverImages")
+        if not isinstance(covers, list):
+            continue
+        for cover in covers:
+            if not isinstance(cover, dict):
+                continue
+            url = _clean_text(cover.get("url"))
+            if url and url not in urls:
+                urls.append(url)
+    return urls
+
+
+def _version_trigger_words(versions: list[dict[str, Any]]) -> list[str]:
+    """Return the first non-empty trigger-word list across *versions*."""
+
+    for version in versions:
+        model_version = version.get("modelVersion")
+        raw = (
+            model_version.get("triggerWords")
+            if isinstance(model_version, dict)
+            else None
+        )
+        words = _parse_trigger_words(raw)
+        if words:
+            return words
+    return []
+
+
+def _parse_trigger_words(raw: Any) -> list[str]:
+    """Decode ModelScope's JSON-encoded trigger-word string list."""
+
+    if isinstance(raw, list):
+        candidates = raw
+    elif isinstance(raw, str) and raw.strip():
+        try:
+            decoded = json.loads(raw)
+        except (json.JSONDecodeError, TypeError):
+            return []
+        if not isinstance(decoded, list):
+            return []
+        candidates = decoded
+    else:
+        return []
+
+    words: list[str] = []
+    for item in candidates:
+        word = _clean_text(item)
+        if not word or word.lower() in _EMPTY_TRIGGER_VALUES:
+            continue
+        if word not in words:
+            words.append(word)
+    return words

tests/services/test_model_sources.py

@@ -332,6 +332,190 @@ class TestAssetBaseUrl:
         )
 
 
+# ---------------------------------------------------------------------------
+# Model card context (site extras kept outside the README)
+# ---------------------------------------------------------------------------
+
+
+def _modelscope_detail_payload() -> dict:
+    """A trimmed-but-faithful ModelScope model-detail response.
+
+    Mirrors the shape of ``/api/v1/models/{id}`` for an AIGC LoRA repo whose
+    README is auto-generated boilerplate, so the author summary and the
+    per-file example images are only reachable through this API.
+    """
+
+    return {
+        "Code": 200,
+        "Data": {
+            "Name": "Krea-2-LORA",
+            "ChineseName": "krea脸模",
+            "Description": "权重0.5-1.2。配合《风格滤镜》lora一起使用。",
+            "BaseModel": ["krea/Krea-2-Turbo"],
+            "License": "Apache License 2.0",
+            "OfficialTags": [
+                {"Tag": "photography", "ChineseName": "写实摄影"},
+                {"Tag": "woman", "ChineseName": "女生"},
+                {"Tag": "photography", "ChineseName": "重复项"},
+            ],
+            "MuseInfo": {
+                "versions": [
+                    {
+                        "stats": {"fileList": ["Krea-2-LORA_c1-st8000.safetensors"]},
+                        "modelVersion": {"showName": "c1-st8000", "triggerWords": '[""]'},
+                        "coverImages": [
+                            {"url": "https://resources.modelscope.cn/cover-images/a.png"}
+                        ],
+                    },
+                    {
+                        "stats": {"fileList": ["Krea-2-LORA_c1-st1000.safetensors"]},
+                        "modelVersion": {
+                            "showName": "c1-st1000",
+                            "triggerWords": '["kreaface","kreamodel"]',
+                        },
+                        "coverImages": [
+                            {"url": "https://resources.modelscope.cn/cover-images/b.png"},
+                            {"url": "https://resources.modelscope.cn/cover-images/c.png"},
+                        ],
+                    },
+                ]
+            },
+        },
+    }
+
+
+class TestFetchModelCardContext:
+    @pytest.mark.asyncio
+    async def test_modelscope_reads_description_tags_and_base_model(self, monkeypatch):
+        async def fake_fetch_json(url, **_kwargs):
+            assert url == "https://modelscope.cn/api/v1/models/u/r"
+            return 200, _modelscope_detail_payload()
+
+        monkeypatch.setattr(
+            "py.services.model_sources.modelscope.fetch_json", fake_fetch_json
+        )
+
+        context = await ModelScopeSource().fetch_model_card_context("u/r")
+
+        assert context.description == "权重0.5-1.2。配合《风格滤镜》lora一起使用。"
+        assert context.base_model == "krea/Krea-2-Turbo"
+        # OfficialTag values only, de-duplicated, order preserved.
+        assert context.official_tags == ["photography", "woman"]
+
+    @pytest.mark.asyncio
+    async def test_modelscope_matches_example_images_by_filename(self, monkeypatch):
+        async def fake_fetch_json(url, **_kwargs):
+            return 200, _modelscope_detail_payload()
+
+        monkeypatch.setattr(
+            "py.services.model_sources.modelscope.fetch_json", fake_fetch_json
+        )
+
+        context = await ModelScopeSource().fetch_model_card_context(
+            "u/r", "Krea-2-LORA_c1-st1000.safetensors"
+        )
+
+        # Only the requested file's images, never a sibling checkpoint's.
+        assert context.example_images == [
+            "https://resources.modelscope.cn/cover-images/b.png",
+            "https://resources.modelscope.cn/cover-images/c.png",
+        ]
+        assert context.trigger_words == ["kreaface", "kreamodel"]
+
+    @pytest.mark.asyncio
+    async def test_modelscope_never_borrows_images_for_an_unknown_file(
+        self, monkeypatch
+    ):
+        async def fake_fetch_json(url, **_kwargs):
+            return 200, _modelscope_detail_payload()
+
+        monkeypatch.setattr(
+            "py.services.model_sources.modelscope.fetch_json", fake_fetch_json
+        )
+
+        context = await ModelScopeSource().fetch_model_card_context(
+            "u/r", "other.safetensors"
+        )
+
+        assert context.example_images == []
+        assert context.trigger_words == []
+        # The repo-wide fields are still returned.
+        assert context.base_model == "krea/Krea-2-Turbo"
+
+    @pytest.mark.asyncio
+    async def test_modelscope_single_version_repo_without_filename(self, monkeypatch):
+        payload = _modelscope_detail_payload()
+        versions = payload["Data"]["MuseInfo"]["versions"]
+        payload["Data"]["MuseInfo"]["versions"] = versions[:1]
+
+        async def fake_fetch_json(url, **_kwargs):
+            return 200, payload
+
+        monkeypatch.setattr(
+            "py.services.model_sources.modelscope.fetch_json", fake_fetch_json
+        )
+
+        context = await ModelScopeSource().fetch_model_card_context("u/r")
+
+        assert context.example_images == [
+            "https://resources.modelscope.cn/cover-images/a.png"
+        ]
+
+    @pytest.mark.asyncio
+    async def test_modelscope_tolerates_failures_and_odd_payloads(self, monkeypatch):
+        payloads = (None, {"Code": 500}, {"Data": "nope"}, {"Data": {}})
+        for payload in payloads:
+
+            async def fake_fetch_json(url, _payload=payload, **_kwargs):
+                return (0 if _payload is None else 200), _payload
+
+            monkeypatch.setattr(
+                "py.services.model_sources.modelscope.fetch_json", fake_fetch_json
+            )
+            context = await ModelScopeSource().fetch_model_card_context(
+                "u/r", "a.safetensors"
+            )
+            assert context.is_empty(), payload
+
+    @pytest.mark.asyncio
+    async def test_modelscope_reads_stats_from_json_encoded_fallback(self, monkeypatch):
+        payload = {
+            "Data": {
+                "MuseInfo": {
+                    "versions": [
+                        {
+                            "modelVersion": {
+                                "showName": "v1",
+                                "stats": '{"fileList": ["model.safetensors"]}',
+                                "triggerWords": '["hi"]',
+                            },
+                            "coverImages": [{"url": "https://cdn.example/x.png"}],
+                        }
+                    ]
+                }
+            }
+        }
+
+        async def fake_fetch_json(url, **_kwargs):
+            return 200, payload
+
+        monkeypatch.setattr(
+            "py.services.model_sources.modelscope.fetch_json", fake_fetch_json
+        )
+
+        context = await ModelScopeSource().fetch_model_card_context(
+            "u/r", "model.safetensors"
+        )
+
+        assert context.example_images == ["https://cdn.example/x.png"]
+        assert context.trigger_words == ["hi"]
+
+    @pytest.mark.asyncio
+    async def test_default_context_is_empty_for_other_sources(self):
+        assert (await HuggingFaceSource().fetch_model_card_context("u/r")).is_empty()
+        assert (await TensorArtSource().fetch_model_card_context("123")).is_empty()
+
+
 # ---------------------------------------------------------------------------
 # Download support
 # ---------------------------------------------------------------------------

tests/services/test_post_processor.py

@@ -6,12 +6,14 @@ functions and verify the business logic (conditions, merges, dispatch).
 
 from __future__ import annotations
 
+import json
 from datetime import datetime, timezone
 from unittest import mock
 
 import pytest
 
 from py.services.agent.post_processor import PostProcessor
+from py.services.model_sources import ModelCardContext
 
 
 @pytest.fixture
@@ -523,3 +525,481 @@ class TestMergeTags:
         result = PostProcessor._merge_tags(existing, new)
         # All tags are lowercased (matching TagUpdateService behaviour)
         assert result == ["anime", "flux", "lora"]
+
+
+# ======================================================================
+# enrich_hf_metadata — site-provided card extras (ModelCardContext)
+# ======================================================================
+
+
+class TestSiteProvidedContext:
+    """ModelScope keeps the author summary, the curated tags and the per-file
+    example images outside the README; these tests pin how they are applied.
+    """
+
+    MODELSCOPE_METADATA = {
+        "from_civitai": False,
+        "source_platform": "modelscope",
+        "source_url": "https://modelscope.cn/models/user/repo",
+    }
+
+    LLM_OUTPUT = {
+        "base_model": "",
+        "trigger_words": [],
+        "short_description": "",
+        "tags": [],
+        "recommended_width": 0,
+        "recommended_height": 0,
+        "preview_url": "",
+        "confidence": "medium",
+    }
+
+    @pytest.mark.asyncio
+    async def test_example_images_become_gallery_and_preview(self, processor):
+        """A boilerplate README still yields images and a downloaded preview."""
+        context = ModelCardContext(
+            example_images=[
+                "https://resources.modelscope.cn/cover-images/a.png",
+                "https://resources.modelscope.cn/cover-images/b.png",
+            ]
+        )
+        boilerplate = "### 当前模型的贡献者未提供更加详细的模型介绍。\n"
+
+        with (
+            mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
+            mock.patch("py.metadata_ops.download_preview") as mock_dl,
+            mock.patch("py.metadata_ops.refresh_cache"),
+        ):
+            mock_dl.return_value = "/p.webp"
+            result = await processor.process(
+                skill_name="enrich_hf_metadata",
+                model_path="/p.safetensors",
+                llm_output=self.LLM_OUTPUT,
+                metadata=dict(self.MODELSCOPE_METADATA),
+                readme_content=boilerplate,
+                source_context=context,
+            )
+
+        applied = mock_apply.call_args[0][1]
+        images = applied["civitai"]["images"]
+        assert [img["url"] for img in images] == context.example_images
+        assert images[0]["type"] == "image"
+        # The first (per-file) site image is used as the preview.
+        mock_dl.assert_awaited_once_with(
+            "/p.safetensors", "https://resources.modelscope.cn/cover-images/a.png"
+        )
+        assert applied["preview_url"] == "/p.webp"
+        assert result["preview_downloaded"] is True
+
+    @pytest.mark.asyncio
+    async def test_example_images_work_without_any_readme(self, processor):
+        """The site images alone are enough — the README may be unreachable."""
+        context = ModelCardContext(
+            example_images=["https://resources.modelscope.cn/cover-images/a.png"]
+        )
+
+        with (
+            mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
+            mock.patch("py.metadata_ops.download_preview", return_value=None),
+            mock.patch("py.metadata_ops.refresh_cache"),
+        ):
+            await processor.process(
+                skill_name="enrich_hf_metadata",
+                model_path="/p.safetensors",
+                llm_output=self.LLM_OUTPUT,
+                metadata=dict(self.MODELSCOPE_METADATA),
+                readme_content="",
+                source_context=context,
+            )
+
+        images = mock_apply.call_args[0][1]["civitai"]["images"]
+        assert [img["url"] for img in images] == context.example_images
+
+    @pytest.mark.asyncio
+    async def test_site_description_precedes_readme_in_model_description(self, processor):
+        context = ModelCardContext(description="权重0.5-1.2。配合滤镜lora一起使用。")
+        readme = "# 模型介绍\n\n本模型依托魔搭社区完成训练。\n"
+
+        with (
+            mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
+            mock.patch("py.metadata_ops.download_preview", return_value=None),
+            mock.patch("py.metadata_ops.refresh_cache"),
+        ):
+            await processor.process(
+                skill_name="enrich_hf_metadata",
+                model_path="/p.safetensors",
+                llm_output=self.LLM_OUTPUT,
+                metadata=dict(self.MODELSCOPE_METADATA),
+                readme_content=readme,
+                source_context=context,
+            )
+
+        description = mock_apply.call_args[0][1]["modelDescription"]
+        assert description.startswith(f"<p>{context.description}</p>")
+        assert "<h1>模型介绍</h1>" in description
+
+    @pytest.mark.asyncio
+    async def test_site_description_is_html_escaped(self, processor):
+        context = ModelCardContext(description="a < b & c")
+
+        with (
+            mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
+            mock.patch("py.metadata_ops.download_preview", return_value=None),
+            mock.patch("py.metadata_ops.refresh_cache"),
+        ):
+            await processor.process(
+                skill_name="enrich_hf_metadata",
+                model_path="/p.safetensors",
+                llm_output=self.LLM_OUTPUT,
+                metadata=dict(self.MODELSCOPE_METADATA),
+                readme_content="",
+                source_context=context,
+            )
+
+        assert mock_apply.call_args[0][1]["modelDescription"] == "<p>a &lt; b &amp; c</p>"
+
+    @pytest.mark.asyncio
+    async def test_site_trigger_words_fill_in_when_llm_finds_none(self, processor):
+        context = ModelCardContext(trigger_words=["kreaface", "kreamodel"])
+        readme = "---\ninstance_prompt: yamlword\n---\nbody\n"
+
+        with (
+            mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
+            mock.patch("py.metadata_ops.download_preview", return_value=None),
+            mock.patch("py.metadata_ops.refresh_cache"),
+        ):
+            await processor.process(
+                skill_name="enrich_hf_metadata",
+                model_path="/p.safetensors",
+                llm_output=self.LLM_OUTPUT,
+                metadata=dict(self.MODELSCOPE_METADATA),
+                readme_content=readme,
+                source_context=context,
+            )
+
+        # The per-file site value wins over the repo-wide YAML instance_prompt.
+        assert mock_apply.call_args[0][1]["civitai"]["trainedWords"] == [
+            "kreaface",
+            "kreamodel",
+        ]
+
+    @pytest.mark.asyncio
+    async def test_yaml_instance_prompt_still_used_when_site_has_none(self, processor):
+        context = ModelCardContext(description="summary only")
+        readme = "---\ninstance_prompt: yamlword\n---\nbody\n"
+
+        with (
+            mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
+            mock.patch("py.metadata_ops.download_preview", return_value=None),
+            mock.patch("py.metadata_ops.refresh_cache"),
+        ):
+            await processor.process(
+                skill_name="enrich_hf_metadata",
+                model_path="/p.safetensors",
+                llm_output=self.LLM_OUTPUT,
+                metadata=dict(self.MODELSCOPE_METADATA),
+                readme_content=readme,
+                source_context=context,
+            )
+
+        assert mock_apply.call_args[0][1]["civitai"]["trainedWords"] == ["yamlword"]
+
+    @pytest.mark.asyncio
+    async def test_site_images_are_skipped_for_a_model_with_no_external_source(
+        self, processor
+    ):
+        """A CivitAI-only model must not pick up ModelScope images."""
+        context = ModelCardContext(
+            example_images=["https://resources.modelscope.cn/cover-images/a.png"]
+        )
+
+        with (
+            mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
+            mock.patch("py.metadata_ops.download_preview", return_value=None),
+            mock.patch("py.metadata_ops.refresh_cache"),
+        ):
+            await processor.process(
+                skill_name="enrich_hf_metadata",
+                model_path="/p.safetensors",
+                llm_output=self.LLM_OUTPUT,
+                metadata={"from_civitai": True},
+                readme_content="",
+                source_context=context,
+            )
+
+        assert "images" not in mock_apply.call_args[0][1].get("civitai", {})
+
+    @pytest.mark.asyncio
+    async def test_site_images_deduplicate_against_readme_images(self, processor):
+        """A URL present in both the site data and the README appears once."""
+        shared = "https://modelscope.cn/models/user/repo/resolve/master/sample.png"
+        context = ModelCardContext(example_images=[shared])
+        readme = f"![alt]({shared})\n"
+
+        with (
+            mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
+            mock.patch("py.metadata_ops.download_preview", return_value=None),
+            mock.patch("py.metadata_ops.refresh_cache"),
+        ):
+            await processor.process(
+                skill_name="enrich_hf_metadata",
+                model_path="/p.safetensors",
+                llm_output=self.LLM_OUTPUT,
+                metadata=dict(self.MODELSCOPE_METADATA),
+                readme_content=readme,
+                source_context=context,
+            )
+
+        images = mock_apply.call_args[0][1]["civitai"]["images"]
+        assert [img["url"] for img in images] == [shared]
+
+    @pytest.mark.asyncio
+    async def test_empty_context_keeps_readme_only_behaviour(self, processor):
+        """An empty site context must not change existing HF behaviour."""
+        readme = "---\nwidget:\n- text: a cat\n  output:\n    url: images/cat.png\n---\n"
+        with (
+            mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
+            mock.patch("py.metadata_ops.download_preview", return_value=None),
+            mock.patch("py.metadata_ops.refresh_cache"),
+        ):
+            await processor.process(
+                skill_name="enrich_hf_metadata",
+                model_path="/p.safetensors",
+                llm_output=self.LLM_OUTPUT,
+                metadata={
+                    "from_civitai": False,
+                    "hf_url": "https://huggingface.co/user/repo",
+                },
+                readme_content=readme,
+                source_context=ModelCardContext(),
+            )
+        images = mock_apply.call_args[0][1]["civitai"]["images"]
+        assert [img["url"] for img in images] == [
+            "https://huggingface.co/user/repo/resolve/main/images/cat.png"
+        ]
+
+
+
+# ======================================================================
+# enrich_hf_metadata — deterministic fallbacks used when the LLM is skipped
+# ======================================================================
+
+
+class TestDeterministicFallbacks:
+    """With the LLM skipped, these fields must still be produced from the API."""
+
+    MODELSCOPE_METADATA = {
+        "from_civitai": False,
+        "source_platform": "modelscope",
+        "source_url": "https://modelscope.cn/models/user/repo",
+    }
+
+    EMPTY_LLM = {
+        "base_model": "",
+        "trigger_words": [],
+        "short_description": "",
+        "tags": [],
+        "recommended_width": 0,
+        "recommended_height": 0,
+        "preview_url": "",
+        "notes": "",
+        "usage_tips": "{}",
+        "confidence": "",
+    }
+
+    @pytest.mark.asyncio
+    async def test_resolved_base_model_used_when_llm_gave_none(self, processor):
+        with (
+            mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
+            mock.patch("py.metadata_ops.download_preview", return_value=None),
+            mock.patch("py.metadata_ops.refresh_cache"),
+        ):
+            await processor.process(
+                skill_name="enrich_hf_metadata",
+                model_path="/p.safetensors",
+                llm_output=self.EMPTY_LLM,
+                metadata=dict(self.MODELSCOPE_METADATA),
+                source_context=ModelCardContext(base_model="krea/Krea-2-Turbo"),
+                resolved_base_model="Krea 2",
+            )
+        assert mock_apply.call_args[0][1]["base_model"] == "Krea 2"
+
+    @pytest.mark.asyncio
+    async def test_llm_base_model_still_wins_over_the_resolver(self, processor):
+        llm = {**self.EMPTY_LLM, "base_model": "Flux.1 D"}
+        with (
+            mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
+            mock.patch("py.metadata_ops.download_preview", return_value=None),
+            mock.patch("py.metadata_ops.refresh_cache"),
+        ):
+            await processor.process(
+                skill_name="enrich_hf_metadata",
+                model_path="/p.safetensors",
+                llm_output=llm,
+                metadata=dict(self.MODELSCOPE_METADATA),
+                resolved_base_model="Krea 2",
+            )
+        assert mock_apply.call_args[0][1]["base_model"] == "Flux.1 D"
+
+    @pytest.mark.asyncio
+    async def test_site_description_fills_civitai_description(self, processor):
+        context = ModelCardContext(description="一个 Krea 2 人像 LoRA。")
+        with (
+            mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
+            mock.patch("py.metadata_ops.download_preview", return_value=None),
+            mock.patch("py.metadata_ops.refresh_cache"),
+        ):
+            await processor.process(
+                skill_name="enrich_hf_metadata",
+                model_path="/p.safetensors",
+                llm_output=self.EMPTY_LLM,
+                metadata=dict(self.MODELSCOPE_METADATA),
+                source_context=context,
+            )
+        assert (
+            mock_apply.call_args[0][1]["civitai"]["description"]
+            == "一个 Krea 2 人像 LoRA。"
+        )
+
+    @pytest.mark.asyncio
+    async def test_llm_short_description_wins_over_site_description(self, processor):
+        llm = {**self.EMPTY_LLM, "short_description": "from the LLM"}
+        with (
+            mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
+            mock.patch("py.metadata_ops.download_preview", return_value=None),
+            mock.patch("py.metadata_ops.refresh_cache"),
+        ):
+            await processor.process(
+                skill_name="enrich_hf_metadata",
+                model_path="/p.safetensors",
+                llm_output=llm,
+                metadata=dict(self.MODELSCOPE_METADATA),
+                source_context=ModelCardContext(description="from the site"),
+            )
+        assert mock_apply.call_args[0][1]["civitai"]["description"] == "from the LLM"
+
+    @pytest.mark.asyncio
+    async def test_official_tags_are_applied_without_the_llm(self, processor):
+        context = ModelCardContext(
+            official_tags=["photography", "character-enhancement", "woman"]
+        )
+        with (
+            mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
+            mock.patch("py.metadata_ops.download_preview", return_value=None),
+            mock.patch("py.metadata_ops.refresh_cache"),
+        ):
+            await processor.process(
+                skill_name="enrich_hf_metadata",
+                model_path="/p.safetensors",
+                llm_output=self.EMPTY_LLM,
+                metadata=dict(self.MODELSCOPE_METADATA),
+                source_context=context,
+            )
+        assert mock_apply.call_args[0][1]["tags"] == [
+            "photography",
+            "character-enhancement",
+            "woman",
+        ]
+
+    @pytest.mark.asyncio
+    async def test_official_tags_are_kept_alongside_llm_tags(self, processor):
+        context = ModelCardContext(official_tags=["photography", "woman"])
+        llm = {**self.EMPTY_LLM, "tags": ["portrait", "photography"]}
+        with (
+            mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
+            mock.patch("py.metadata_ops.download_preview", return_value=None),
+            mock.patch("py.metadata_ops.refresh_cache"),
+        ):
+            await processor.process(
+                skill_name="enrich_hf_metadata",
+                model_path="/p.safetensors",
+                llm_output=llm,
+                metadata=dict(self.MODELSCOPE_METADATA),
+                source_context=context,
+            )
+        # Site tags first, then the LLM's extra ones, no duplicates.
+        assert mock_apply.call_args[0][1]["tags"] == [
+            "photography",
+            "woman",
+            "portrait",
+        ]
+
+    @pytest.mark.asyncio
+    async def test_usage_tips_recovered_from_the_author_summary(self, processor):
+        context = ModelCardContext(
+            description="权重0.5-1.2。2个一起时，权重建议都用1.0-1.1。"
+        )
+        with (
+            mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
+            mock.patch("py.metadata_ops.download_preview", return_value=None),
+            mock.patch("py.metadata_ops.refresh_cache"),
+        ):
+            await processor.process(
+                skill_name="enrich_hf_metadata",
+                model_path="/p.safetensors",
+                llm_output=self.EMPTY_LLM,
+                metadata=dict(self.MODELSCOPE_METADATA),
+                source_context=context,
+            )
+        tips = json.loads(mock_apply.call_args[0][1]["usage_tips"])
+        assert tips == {
+            "strength_min": 0.5,
+            "strength_max": 1.2,
+            "strength_range": "0.5-1.2",
+        }
+
+    @pytest.mark.asyncio
+    async def test_llm_usage_tips_win_over_the_regex(self, processor):
+        llm = {**self.EMPTY_LLM, "usage_tips": '{"strength": 0.9}'}
+        with (
+            mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
+            mock.patch("py.metadata_ops.download_preview", return_value=None),
+            mock.patch("py.metadata_ops.refresh_cache"),
+        ):
+            await processor.process(
+                skill_name="enrich_hf_metadata",
+                model_path="/p.safetensors",
+                llm_output=llm,
+                metadata=dict(self.MODELSCOPE_METADATA),
+                source_context=ModelCardContext(description="权重0.5-1.2"),
+            )
+        assert mock_apply.call_args[0][1]["usage_tips"] == '{"strength": 0.9}'
+
+    @pytest.mark.asyncio
+    async def test_notes_are_not_rewritten_when_the_llm_is_skipped(self, processor):
+        """Notes are LLM-only; skipping must not clobber or duplicate them."""
+        with (
+            mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
+            mock.patch("py.metadata_ops.download_preview", return_value=None),
+            mock.patch("py.metadata_ops.refresh_cache"),
+        ):
+            await processor.process(
+                skill_name="enrich_hf_metadata",
+                model_path="/p.safetensors",
+                llm_output=self.EMPTY_LLM,
+                metadata={**self.MODELSCOPE_METADATA, "notes": "existing notes"},
+                source_context=ModelCardContext(description="权重0.5-1.2"),
+            )
+        assert "notes" not in mock_apply.call_args[0][1]
+
+    @pytest.mark.asyncio
+    async def test_no_site_data_leaves_llm_only_fields_untouched(self, processor):
+        """An empty context must behave exactly like the pre-existing pipeline."""
+        with (
+            mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
+            mock.patch("py.metadata_ops.download_preview", return_value=None),
+            mock.patch("py.metadata_ops.refresh_cache"),
+        ):
+            await processor.process(
+                skill_name="enrich_hf_metadata",
+                model_path="/p.safetensors",
+                llm_output=self.EMPTY_LLM,
+                metadata=dict(self.MODELSCOPE_METADATA),
+                source_context=ModelCardContext(),
+                resolved_base_model="",
+            )
+        applied = mock_apply.call_args[0][1]
+        assert "base_model" not in applied
+        assert "tags" not in applied
+        assert "notes" not in applied
+        assert "usage_tips" not in applied