63785f82b5
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
3.3 KiB
name, title, description, llm_required
| name | title | description | llm_required |
|---|---|---|---|
| enrich_hf_metadata | Enrich Metadata from HuggingFace | Parse the HuggingFace model card via LLM to extract description, trigger words, base model, tags, and preview image URL. | 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
- Repository: {{hf_url}}
- Model file path: {{model_path}}
- Repository ID: {{repo}}
Current Metadata (may be incomplete)
{{current_metadata}}
Available Base Models
The following base models are currently valid in this system: {{base_models}}
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 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.
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. ) 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