Dify DSL 工作流生成器

专业的 Dify 工作流 DSL/YML 文件自动生成工具，基于对 86+ 实际工作流案例的深度学习，能够根据用户的业务需求自动生成符合 Dify 规范的完整工作流配置文件。

核心功能

• ✅ 完整DSL生成: 自动生成包含 app、dependencies、workflow 的完整 YML 文件
• ✅ 多节点支持: 支持 start、llm、answer、code、http-request、if-else、tool 等所有节点类型
• ✅ 智能连接: 自动生成节点间的 edges 连接关系
• ✅ 参数配置: 智能推荐模型参数、提示词配置
• ✅ 插件集成: 自动识别并配置所需的 Dify 插件依赖
• ✅ 规范格式: 严格遵循 Dify 0.3.0 版本的 DSL 规范

使用方法

基础用法

生成一个 Dify 工作流用于 [业务需求描述]

详细用法

帮我生成一个 Dify 工作流 DSL 文件:
- 功能: [工作流要实现的功能]
- 输入: [用户输入的内容]
- 处理步骤: [详细的处理逻辑]
- 输出: [期望的输出结果]
- 使用插件: [需要的插件，可选]

Dify DSL 文件结构

基于对 86+ 真实工作流案例的学习，Dify DSL YML 文件遵循以下结构:

1. App 配置

app:
  description: '工作流描述'
  icon: 🤖
  icon_background: '#FFEAD5'
  mode: advanced-chat  # 或 workflow, agent-chat
  name: 工作流名称
  use_icon_as_answer_icon: false

模式说明:

• advanced-chat: 高级对话模式(chatflow)
• workflow: 工作流模式
• agent-chat: AI Agent 模式

2. Dependencies 依赖

dependencies:
- current_identifier: null
  type: marketplace
  value:
    marketplace_plugin_unique_identifier: 插件唯一标识符

常用插件:

• langgenius/openai_api_compatible: OpenAI 兼容接口
• bowenliang123/md_exporter: Markdown 导出器
• 其他市场插件根据需求添加

3. Workflow 工作流

kind: app
version: 0.3.0
workflow:
  conversation_variables: []
  environment_variables: []
  features:
    file_upload:
      enabled: false
    speech_to_text:
      enabled: false
    text_to_speech:
      enabled: false
  graph:
    edges: []
    nodes: []

节点类型详解

Start 开始节点

- data:
    desc: ''
    title: 开始
    type: start
    variables:
    - label: 用户输入
      max_length: 1000
      options: []
      required: true
      type: paragraph  # 或 text-input, select, file
      variable: query
  id: 'start'
  position:
    x: 100
    y: 300
  type: custom
  width: 244
  height: 90

变量类型:

• paragraph: 段落文本(多行)
• text-input: 单行文本
• select: 下拉选择
• file: 文件上传
• number: 数字

LLM 大语言模型节点

- data:
    context:
      enabled: false
      variable_selector: []
    model:
      completion_params:
        temperature: 0.7
        max_tokens: 2000
      mode: chat
      name: gpt-4
      provider: openai
    prompt_template:
    - id: 唯一ID
      role: system
      text: 系统提示词
    - id: 唯一ID
      role: user
      text: 用户提示词 {{#变量引用#}}
    title: LLM节点
    type: llm
    vision:
      enabled: false
  id: '节点ID'
  position:
    x: 400
    y: 300
  type: custom

常用模型provider:

• openai: OpenAI
• langgenius/openai_api_compatible/openai_api_compatible: 兼容接口
• anthropic: Claude
• alibaba: 通义千问

变量引用格式:

• {{#节点ID.输出变量#}}: 引用其他节点的输出
• {{#sys.query#}}: 引用系统变量(用户输入)
• {{#节点ID.text#}}: 引用LLM输出文本

Code 代码执行节点

- data:
    code: |
      import json

      def main(arg1: str, arg2: str) -> dict:
          # 处理逻辑
          result = process(arg1, arg2)
          return {
              "result": result,
              "status": "success"
          }
    code_language: python3
    outputs:
      result:
        type: string
      status:
        type: string
    title: 代码执行
    type: code
    variables:
    - value_selector:
      - '前置节点ID'
      - 输出变量
      variable: arg1
  id: '节点ID'
  position:
    x: 700
    y: 300
  type: custom

代码语言:

• python3: Python 3
• javascript: JavaScript (部分版本支持)

输出类型:

• string: 字符串
• number: 数字
• object: 对象
• array[string]: 字符串数组
• array[number]: 数字数组
• array[object]: 对象数组

HTTP Request 节点

- data:
    authorization:
      config: null
      type: no-auth
    body:
      data: '{"key": "{{#变量#}}"}'
      type: json
    headers: ''
    method: post
    timeout:
      max_connect_timeout: 0
      max_read_timeout: 0
      max_write_timeout: 0
    title: HTTP请求
    type: http-request
    url: https://api.example.com/endpoint
  id: '节点ID'
  position:
    x: 1000
    y: 300
  type: custom

HTTP方法:

• get: GET 请求
• post: POST 请求
• put: PUT 请求
• patch: PATCH 请求
• delete: DELETE 请求

认证类型:

• no-auth: 无认证
• api-key: API Key
• bearer: Bearer Token

If-Else 条件判断节点

- data:
    cases:
    - case_id: case1
      conditions:
      - comparison_operator: contains
        id: 条件ID
        value: 期望值
        variable_selector:
        - '节点ID'
        - 变量名
      id: case1
      logical_operator: and
    logical_operator: or
    title: 条件判断
    type: if-else
  id: '节点ID'
how to use dify-dsl-generator
CursorClaude CodeClineCodexWindsurfGooseGitHub CopilotVS CodeGemini CLIKiloOpencodeKimi CLIRoo ClineAiderContinueZedBolt.newLovablev0Replit AgentSweepSmol DeveloperDevinCavemanAmpAntigravity
How to use dify-dsl-generator on Cursor
AI-first code editor with Composer
1
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
›Cursor installed and configured on your development machine
›Node.js version 16.0+ with npm package manager (verify with node --version)
›Active project directory or workspace where you want to add dify-dsl-generator
2
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/wwwzhouhui/skills_collection --skill dify-dsl-generatorcopy
The skills CLI fetches dify-dsl-generator from GitHub repository wwwzhouhui/skills_collection and configures it for Cursor.
3
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│   • Amp
│   • Antigravity
│   • Cline
│   • Codex
│ ●Cursor(selected)
│   • Cursor
│   • Windsurf
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/dify-dsl-generator
Reload or restart Cursor to activate dify-dsl-generator. Access the skill through slash commands (e.g., /dify-dsl-generator) or your agent's skill management interface.
⚠
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
Additional Resources
›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?