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add-model-descriptions

Add descriptions for new models from the HuggingFace router to chat-ui configuration. Use when new models are released on the router and need descriptions added to prod.yaml and dev.yaml. Triggers on requests like "add new model descriptions", "update models from router", "sync models", or when explicitly invoking /add-model-descriptions.

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包含的文件(1)

  • SKILL.md2.3 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Add Model Descriptions

Add descriptions for new models available in the HuggingFace router to chat-ui's prod.yaml and dev.yaml.

Workflow

  1. Fetch models from router

    WebFetch https://router.huggingface.co/v1/models
    

    Extract all model IDs from the response.

  2. Read current configuration

    • Read chart/env/prod.yaml
    • Extract model IDs from the MODELS JSON array in envVars
  3. Identify missing models Compare router models with prod.yaml. Missing = in router but not in prod.yaml.

  4. Research each missing model For each missing model, search the web for its specifications:

    • Model architecture (dense, MoE, parameters)
    • Key capabilities (coding, reasoning, vision, multilingual, etc.)
    • Target use cases
  5. Write descriptions Match existing style:

    • 8-12 words
    • Sentence fragments (no period needed)
    • No articles ("a", "the") unless necessary
    • Focus on: architecture, specialization, key capability

    Examples:

    • "Flagship GLM MoE for coding, reasoning, and agentic tool use."
    • "MoE agent model with multilingual coding and fast outputs."
    • "Vision-language Qwen for documents, GUI agents, and visual reasoning."
    • "Mobile agent for multilingual Android device automation."
  6. Update both files Add new models at the TOP of the MODELS array in:

    • chart/env/prod.yaml
    • chart/env/dev.yaml

    Format:

    { "id": "org/model-name", "description": "Description here." }
    
  7. Commit changes

    git add chart/env/prod.yaml chart/env/dev.yaml
    git commit -m "feat: add descriptions for N new models from router"
    

Notes

  • FP8 variants: describe as "FP8 [base model] for efficient inference with [key capability]"
  • Vision models: mention "vision-language" and key visual tasks
  • Agent models: mention "agent" and automation capabilities
  • Regional models: mention language focus (e.g., "European multilingual", "Southeast Asian")

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