refactor(agent): consolidate skill definition into single SKILL.md with YAML frontmatter

Merge skill.yaml (metadata) and prompt.md (prompt template) into a
single SKILL.md file with YAML frontmatter, matching the agent-skill
convention used by opencode and Claude Code.

- Add frontmatter parser (_parse_skill_file) to SkillRegistry
- Remove skill.yaml, prompt.md, empty skills/__init__.py
- Remove obsolete load_handler method
- Update tests for new format and cleaned-up fields

py/services/agent/skills/__init__.py

@@ -1 +0,0 @@
-# Agent skills package — each subdirectory is a skill.

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

@@ -1,3 +1,12 @@
+---
+name: enrich_hf_metadata
+title: "Enrich Metadata from HuggingFace"
+description: >
+  Parse the HuggingFace model card via LLM to extract description, trigger
+  words, base model, tags, and preview image URL.
+llm_required: true
+---
+
 You are an expert assistant for AI image generation models. Your task is to extract structured metadata from a HuggingFace model card (README.md).
 
 ## Model Information
@@ -12,6 +21,11 @@ You are an expert assistant for AI image generation models. Your task is to extr
 {{current_metadata}}
 ```
 
+## Available Base Models
+
+The following base models are currently valid in this system:
+{{base_models}}
+
 ## HuggingFace README Content
 
 ```
@@ -23,10 +37,7 @@ You are an expert assistant for AI image generation models. Your task is to extr
 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 below. Do not invent new names or use aliases.
-
-Available Base Models:
-{{base_models}}
+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.
 
 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.
 
@@ -60,6 +71,7 @@ Your confidence level in the extracted data:
 
 Return ONLY a JSON object with exactly these fields (no markdown fences, no extra text):
 
+```json
 {
   "model_path": "{{model_path}}",
   "base_model": "<canonical name or empty string>",
@@ -69,9 +81,9 @@ Return ONLY a JSON object with exactly these fields (no markdown fences, no extr
   "preview_url": "<image URL or empty string>",
   "confidence": "<high|medium|low>"
 }
+```
 
 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
-- For base_model, the YAML frontmatter often has `base_model:` with a HuggingFace repo name like "black-forest-labs/FLUX.1-dev" — map this to "Flux.1 D"

py/services/agent/skills/enrich_hf_metadata/skill.yaml

@@ -1,47 +0,0 @@
-name: enrich_hf_metadata
-title: "Enrich Metadata from HuggingFace"
-description: >
-  Parse the HuggingFace model card via LLM to extract description, trigger
-  words, base model, tags, and preview image URL.  Updates .metadata.json
-  and downloads the preview thumbnail.
-llm_required: true
-model_type_filter: ["lora", "checkpoint", "embedding"]
-input_schema:
-  type: object
-  properties:
-    model_paths:
-      type: array
-      items:
-        type: string
-  required:
-    - model_paths
-output_schema:
-  type: object
-  properties:
-    model_path:
-      type: string
-    base_model:
-      type: string
-    trigger_words:
-      type: array
-      items:
-        type: string
-    description:
-      type: string
-    tags:
-      type: array
-      items:
-        type: string
-    preview_url:
-      type: string
-    confidence:
-      type: string
-      enum: ["high", "medium", "low"]
-  required:
-    - model_path
-    - confidence
-permissions:
-  write_metadata: true
-  write_previews: true
-  network_domains:
-    - "huggingface.co"