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/enrich_hf_metadata/prompt.md

@@ -1,77 +0,0 @@
-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
-
-- **Repository**: {{hf_url}}
-- **Model file path**: {{model_path}}
-- **Repository ID**: {{repo}}
-
-## Current Metadata (may be incomplete)
-
-```json
-{{current_metadata}}
-```
-
-## HuggingFace README Content
-
-```
-{{readme_content}}
-```
-
-## Extraction Instructions
-
-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}}
-
-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.
-
-### trigger_words
-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.
-
-### 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.
-
-### 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.
-
-### confidence
-Your confidence level in the extracted data:
-- "high" — most fields were explicitly stated in the README
-- "medium" — some fields were inferred from context
-- "low" — most fields are guesses based on limited information
-
-## Output Format
-
-Return ONLY a JSON object with exactly these fields (no markdown fences, no extra text):
-
-{
-  "model_path": "{{model_path}}",
-  "base_model": "<canonical name or empty string>",
-  "trigger_words": ["<word1>", "<word2>"],
-  "description": "<1-2 sentence summary>",
-  "tags": ["<tag1>", "<tag2>"],
-  "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"