twwch/workflow-skill/dify-workflow

Dify Workflow DSL Generator

Overview

This skill generates Dify workflow DSL files that can be directly imported into a Dify instance. Given a natural language description of a desired workflow, it produces a complete YAML (default) or JSON file containing all nodes, edges, layout positions, and configuration.

The skill triggers when the user asks to:

• Create, generate, or build a Dify workflow
• Convert a process description into a Dify DSL
• Scaffold a chatflow or workflow application

Output format is YAML by default (.dify.yml), with JSON (.dify.json) available on request.

Smart Interaction Logic

Before generating, assess whether the user's description is sufficient:

Proceed directly if the description includes:

• Clear input/output expectations
• Processing logic (what the workflow should do)
• Enough detail to select appropriate nodes

Ask clarifying questions (max 3 rounds) if unclear:

1. "What inputs will the workflow receive, and what outputs should it produce?"
2. "What processing steps are needed? (e.g., LLM call, knowledge retrieval, API call, conditional logic)"
3. "Any specific models, tools, or knowledge bases to use?"

Once requirements are clear, proceed to generation.

Node Router Table

Generation Flow

Follow these steps to produce a valid DSL file:

1. Parse requirement -- Identify the app mode (workflow or advanced-chat), needed nodes, and data flow.

• Use workflow mode with Start/End nodes for batch processing tasks.
• Use advanced-chat mode with Start/Answer nodes for conversational chatbots.

1. Select nodes from the router table above. Load the corresponding schema file for each selected node to get the full field specification.

1. Check template match -- If the requirement closely matches a known pattern, start from a template (see Template Matching below). Adapt fields as needed.

1. Assemble from schemas -- If no template matches, build nodes individually. For each node:

• Generate a unique ID (13-digit timestamp string, e.g., "1711536487001").
• Fill required fields per the node schema.
• Use {{#nodeId.variableName#}} syntax for variable references.
• Use {{#sys.query#}} for system query variable in chatflow mode.

1. Generate edges -- Connect nodes following the rules in references/edge-and-layout.md:

• Edge ID format: {sourceId}-{sourceHandle}-{targetId}-{targetHandle}
• Standard sourceHandle: "source" for most nodes
• If/Else branches: "true" (first case), case_id (elif), "false" (else)
• Question Classifier branches: topic id as sourceHandle
• targetHandle is always "target"
• All edges use type: "custom" and zIndex: 0 (or 1002 inside iterations)

1. Calculate layout positions -- Place nodes on a left-to-right grid:

• Start node at {x: 80, y: 282}
• Horizontal spacing: 300px per step (NODE_WIDTH 240 + X_OFFSET 60)
• Vertical spacing for parallel branches: 200px apart
• Node width: 244px, height: varies by node (54-150px typical)

1. Output file -- Render as YAML (default) or JSON. Validate structure completeness.

DSL Structure Quick Reference

version: "0.6.0"
kind: app
app:
  name: "Workflow Name"
  mode: "advanced-chat"           # or "workflow"
  description: "..."
  icon: "\U0001F916"
  icon_background: "#FFEAD5"
  icon_type: emoji
  use_icon_as_answer_icon: false
dependencies: []
workflow:
  environment_variables: []
  conversation_variables: []
  features:
    file_upload:
      enabled: false
    opening_statement: ""         # chatflow only
    retriever_resource:
      enabled: false
    sensitive_word_avoidance:
      enabled: false
    speech_to_text:
      enabled: false
    suggested_questions: []       # chatflow only
    suggested_questions_after_answer:
      enabled: false
    text_to_speech:
      enabled: false
  graph:
    nodes: []                     # Node objects
    edges: []                     # Edge objects
    viewport:
      x: 0
      y: 0
      zoom: 0.7

For the complete field-level specification, see references/dsl-format.md.

Output Rules

• Output location: Final workflow files (.dify.yml / .dify.json) go to current working directory. Any intermediate/temp files go to /tmp/dify-workflow/.
• Filename: <kebab-case-name>.dify.yml (or .dify.json for JSON output)
• Required sections: version, kind, app, workflow (with graph, features)
• Node IDs: 13-digit timestamp strings (e.g., "1711536487001"). Increment by a few thousand between nodes to simulate realistic IDs.
• Coordinates: Start at {x: 80, y: 282}. Each subsequent column at +300 on x-axis. Parallel branches offset on y-axis by +200.
• Variable references: Use {{#nodeId.variableName#}} syntax. System variables use sys prefix: {{#sys.query#}}, {{#sys.user_id#}}.
• Model provider format: "langgenius/<provider>/<provider>" (e.g., "langgenius/openai/openai"). Use a real, currently-shipping model name for model.name (e.g. deepseek-chat, gpt-4o-mini); fictional names like deepseek-v4-pro will fail provider validation.
• All string node IDs must be quoted in YAML to prevent type coercion.

Common Schema Pitfalls (must avoid)

These are the schema mistakes most likely to make a generated DSL fail import in Dify 0.6.0:

1. Variable shape differs per node type. The shape of a single entry inside a node's variable list is not the same across nodes:

• code, llm, template-transform, parameter-extractor use objects:

```yaml variables:

• variable: my_arg

value_selector: ["upstream_id", "field"] value_type: string ```

• variable-aggregator uses bare nested lists (no value_selector: wrapper, no variable: name):

```yaml variables:

• - "branch1_id"
• "output"
• - "branch2_id"
• "output"

```

• document-extractor uses a singular variable_selector (a flat array of strings), not a list:

``yaml variable_selector: ["upstream_id", "field"] ``

1. `memory` belongs only to chatflow LLM nodes. In workflow-mode apps, sys.query does not exist; LLM nodes must omit the memory block. Same applies for LLM nodes placed inside an iteration in a workflow app.
2. Iteration containers need dimensions in both places. Set width and height both inside data and at the outer node level. Iteration-start child uses outer type: custom-iteration-start with data.type: iteration-start.
3. Iteration child nodes must declare parentId: <iteration_id>, data.isInIteration: true, data.iteration_id: <iteration_id>, and zIndex: 1002. Their position is relative to the iteration container (e.g. start near {x: 24, y: 68}).
4. `iterator_input_type` must match the real element type. For files: "array[file]". For numbers: "array[number]". Mismatch breaks runtime variable resolution.
5. Plugin dependencies. If you reference a model provider (e.g. langgenius/deepseek/deepseek) or a tool provider not bundled with Dify, declare it in top-level dependencies so import flags missing plugins clearly. Empty dependencies: [] is fine when the deployment is known to have those plugins installed.
6. End node `outputs` is a list of `{variable, value_selector, value_type}` items; Code node outputs is a dict keyed by variable name with {type, children} values. Do not mix the two shapes.
7. Edge `data` block should include sourceType, targetType, isInIteration, isInLoop. Edges inside iteration also need iteration_id and zIndex: 1002.

Template Matching

Use a template when the user's request closely matches one of these patterns. Load the template, then customize fields (model, prompts, variables) to fit the specific requirement.

If the requirement partially matches, use the closest template as a starting point and add/remove nodes as needed.

Few-Shot Example

A minimal chatflow (Start -> LLM -> Answer):