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Universal Learner

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huangserva
universal-learner

通用学习器 - 从任何领域的Prompt中自动提取可复用元素,持续学习和积累知识

Overview

Publisherhuangserva
Repositoryskill-prompt-generator
Skill nameuniversal-learner
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1.5K
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Bundled files
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  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by huangserva on GitHub. Read the source before you install it.

Installation

Install the Universal Learner AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/huangserva/skill-prompt-generator/tree/main/.claude/skills/universal-learner
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/huangserva/skill-prompt-generator.git /tmp/skill-prompt-generator
mkdir -p .claude/skills
cp -r /tmp/skill-prompt-generator/.claude/skills/universal-learner .claude/skills/universal-learner
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Universal Learner in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Universal Learner on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Universal Learner is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Universal Learner - 通用学习器 Skill

版本: 1.0 架构: Master-Subordinate 学习方式: 半自动(生成报告 + 人工审核)

🎯 核心功能

从任何领域的Prompt中自动提取可复用元素,存入Universal Elements Library数据库。

支持7大领域:

  • 📷 portrait - 人像摄影
  • 🏠 interior - 室内设计
  • 📦 product - 产品摄影
  • 🎨 design - 平面设计
  • 🎭 art - 艺术风格
  • 🎬 video - 视频生成
  • 📸 common - 通用摄影技术

📋 使用方式

方式1:学习单个Prompt

学习这个Prompt: [粘贴完整Prompt]

或

分析并学习: A premium collector's edition book photographed with Phase One camera, featuring Italian calfskin binding...

方式2:批量学习18个源Prompts

批量学习所有Prompts

或

学习extracted_modules.json中的所有Prompts

方式3:学习特定领域

只学习product领域的Prompts

或

学习Prompt #1, #6, #14, #16(产品摄影)

方式4:学习设计系统/模块系统 ⭐ 新增

学习这个设计系统:[粘贴完整的设计系统规范]

或

学习这个工作流模块:[完整工作流内容]

自动识别特征:

  • 包含关键词:模块 / 系统 / 工作流 / 框架
  • 有层级结构(背景系统 + 配色方案 + 字体系统...)
  • 包含设计理念说明或流程步骤

特殊处理(模块系统模式):

  • 提取元素 → elements表
  • 创建设计系统记录 → design_templates表
  • 保存完整原始内容 → visual_reference字段 ⭐ 关键增强

🔄 工作流程

输入Prompt
  ↓
【Step 0: 类型识别】⭐ 新增
  - 判断是"画面描述型" 还是 "设计系统型"
  - 设计系统特征:包含"设计系统"、"风格规范"、有层级结构
  - 输出: {"type": "prompt" | "design_system"}
  ↓
【Step 0.1: 模块系统判断】⭐ 关键增强
  - 检测关键词:【模块】【系统】【工作流】【框架】
  - YES → 模块系统模式(需保存完整原始内容)
  - NO  → 普通学习模式(只提取元素)
  ↓
【Step 1: 领域分类】domain_classifier.md
  - 识别主要领域(product/design/art/video...)
  - 判断是否多领域
  - 输出: {"primary": "product", "secondary": ["photography"]}
  ↓
【Step 2: 元素提取】element_extractor.md
  - 根据领域提取对应元素
  - product → product_types, materials, photography_techniques
  - design → layouts, effects, typography
  - art → art_styles, special_effects
  - 输出: List[{category, name, template, keywords}]
  ↓
【Step 3: 自动打标签】tagger.md
  - 基于keywords生成tags
  - 添加领域标签、类别标签
  - 跨领域标签识别(luxury, glass, dynamic...)
  - 输出: tags列表
  ↓
【Step 4: 计算复用性】
  - reusability_score (1-10)
  - 基于:通用性、清晰度、独立性
  ↓
【Step 5: 更新数据库】library_updater.md
  - 检查是否已存在(去重)
  - 生成element_id
  - 写入elements.db
  - 更新tag_index
  ↓
【Step 5.1: 如果是设计系统】⭐ 新增
  - 同时创建模板记录
  - 写入design_templates表
  - 关联所有提取的元素
  - 保存设计理念、使用指南等完整信息
  ↓
【Step 5.2: 如果是模块系统模式】⭐ 关键增强
  - 把完整原始内容保存到 visual_reference 字段
  - 包括:完整工作流、所有模板、配置参数等
  - 目的:可直接使用,无需重新组合元素
  ↓
【Step 6: 生成报告】
  - 新学习的元素列表
  - 按领域和类别分组
  - 推荐的tags
  - 质量评估
  - (设计系统)额外显示模板信息

📊 输出示例

学习报告

markdown
# Universal Learner - 学习报告

**学习时间**: 2026-01-01 18:30:00
**源Prompt**: Prompt #1

## 🎯 领域识别

主领域: **product** (产品摄影)
次领域: **common** (通用摄影)

## 📦 提取的元素

### 产品类型 (product_types)

1. **collector_edition_book** - 收藏版书籍
   - 模板: premium collector's edition book, luxury binding, Italian calfskin cover
   - 关键词: collector's edition, premium book, luxury binding
   - 标签: product, book, luxury, collectible
   - 复用性: 7.5/10
   - element_id: product_product_types_001

### 材质纹理 (material_textures)

2. **glossy_reflective** - 光泽反射材质
   - 模板: high-end glossy surface, reflective finish, metallic sheen
   - 关键词: glossy, reflective, shiny, polished
   - 标签: material, glossy, luxury, product
   - 复用性: 8.5/10
   - element_id: product_material_textures_002

### 摄影技术 (photography_techniques)

3. **macro_product_shot** - 产品微距特写
   - 模板: Phase One medium format camera with 100mm macro lens, razor-sharp focus
   - 关键词: macro, close-up, detailed, high-resolution
   - 标签: photography, macro, product, technical
   - 复用性: 9.0/10
   - element_id: common_photography_techniques_032

## ✅ 已添加到数据库

- 3个新元素已写入 elements.db
- 更新了12个标签索引
- product领域: 60 → 63 个元素
- common领域: 31 → 32 个元素

## 💡 质量评估

- 提取完整度: 95%
- 标签质量: 优秀
- 复用性评分: 8.3/10 (平均)

🎛️ 配置选项

自动化程度

  • 当前: 半自动(生成报告 + 人工审核)
  • 未来: 可选全自动模式

复用性评分标准

评分标准
9-10极高复用性:跨领域通用,如"macro photography", "soft lighting"
7-8高复用性:领域内通用,如"product on table", "geometric layout"
5-6中等复用性:特定场景,如"collector's edition book"
3-4低复用性:非常具体,如"vintage 1960s typewriter"
1-2极低复用性:一次性描述,不推荐提取

📚 数据源

输入: extracted_results/extracted_modules.json (18个源Prompts)

输出:

  • extracted_results/elements.db (SQLite数据库)
  • extracted_results/universal_elements_library.json (JSON导出)

🔧 模块说明

模块文件功能
领域分类器modules/domain_classifier.md识别Prompt属于哪个领域
元素提取器modules/element_extractor.md提取可复用元素
标签生成器modules/tagger.md自动生成tags
库更新器modules/library_updater.md更新数据库

✅ 验收标准

学习成功的标志:

  • ✅ 能正确识别7大领域
  • ✅ 从18个Prompts提取~440个元素
  • ✅ 自动去重(不重复添加已存在元素)
  • ✅ 标签质量高(相关性强)
  • ✅ 复用性评分合理

Skill状态: ✅ 已实现 最后更新: 2026-01-05 维护者: Universal Library System

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Universal Learner AI skill do?

通用学习器 - 从任何领域的Prompt中自动提取可复用元素,持续学习和积累知识

Why use Universal Learner on TypingMind?

Because you install it once and use it with any model. Universal Learner is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Universal Learner in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/huangserva/skill-prompt-generator/tree/main/.claude/skills/universal-learner. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Universal Learner?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Universal Learner?

As many as you like. As long as a model supports skills, you can use Universal Learner with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Universal Learner AI skill free?

It is published on GitHub by huangserva. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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