dify-workflow

name dify-workflow description Generate Dify workflow DSL files from natural language descriptions. Produces importable YAML/JSON workflow definitions with correct node schemas, edges, and layout. 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: "What inputs will the workflow receive, and what outputs should it produce?" "What processing steps are needed? (e.g., LLM call, knowledge retrieval, API call, conditional logic)" "Any specific models, tools, or knowledge bases to use?" Once requirements are clear, proceed to generation. Node Router Table Node Type Key Purpose Key Params Schema Path Start start Entry point; defines input variables variables references/nodes/start.md End end Terminal node for Workflow mode; declares outputs outputs references/nodes/end.md Answer answer Streams response in Chatflow mode answer , variables references/nodes/answer.md LLM llm Invokes a large language model model , prompt_template , context , vision references/nodes/llm.md Knowledge Retrieval knowledge-retrieval Searches knowledge bases for relevant chunks query_variable_selector , dataset_ids , retrieval_mode references/nodes/knowledge-retrieval.md Code code Executes Python3/JavaScript/JSON code code_language , code , variables , outputs references/nodes/code.md HTTP Request http-request Makes HTTP API calls method , url , headers , body , authorization references/nodes/http-request.md If/Else if-else Conditional branching (IF/ELIF/ELSE) cases references/nodes/if-else.md Variable Aggregator variable-aggregator Merges variables from multiple branches output_type , variables references/nodes/variable-aggregator.md Iteration iteration Loops over array, runs sub-graph per element iterator_selector , iterator_input_type , output_selector , start_node_id references/nodes/iteration.md Document Extractor document-extractor Extracts text from uploaded files (PDF/DOCX/etc.) variable_selector , is_array_file references/nodes/document-extractor.md Template Transform template-transform Renders Jinja2 templates with variables template , variables references/nodes/template-transform.md Question Classifier question-classifier Routes by classifying input into categories via LLM query_variable_selector , model , classes references/nodes/question-classifier.md Parameter Extractor parameter-extractor Extracts structured params from text via LLM query , model , parameters , reasoning_mode references/nodes/parameter-extractor.md Tool tool Invokes external tools (built-in, API, MCP) provider_id , provider_type , tool_name , tool_parameters references/nodes/tool.md Generation Flow Follow these steps to produce a valid DSL file: 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. Select nodes from the router table above. Load the corresponding schema file for each selected node to get the full field specification. Check template match -- If the requirement closely matches a known pattern, start from a template (see Template Matching below). Adapt fields as needed. 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. 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) 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) 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: 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 : 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): variables: - - "branch1_id" - "output" - - "branch2_id" - "output" document-extractor uses a singular variable_selector (a flat array of strings), not a list: variable_selector: [ "upstream_id" , "field" ] 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. 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 . 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} ). iterator_input_type must match the real element type. For files: "array[file]" . For numbers: "array[number]" . Mismatch breaks runtime variable resolution. 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. 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. 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. Template Path Matches When Chatbot references/templates/chatbot.yml Simple conversational bot: Start -> LLM -> Answer RAG references/templates/rag.yml Knowledge-base Q&A: Start -> Knowledge Retrieval -> LLM -> Answer Agent references/templates/agent.yml Tool-using agent with question classification or parameter extraction Translation references/templates/translation.yml Text transformation/translation: Start -> LLM (with specific system prompt) -> Answer/End 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): version: "0.6.0" kind: app app: name: "Simple Chatbot" mode: advanced-chat icon: "\U0001F916" icon_background: "#FFEAD5" icon_type: emoji description: "A minimal chatbot using GPT-4o-mini." use_icon_as_answer_icon: false dependencies: [] workflow: environment_variables: [] conversation_variables: [] features: file_upload: enabled: false opening_statement: "Hello! How can I help you today?" retriever_resource: enabled: false sensitive_word_avoidance: enabled: false speech_to_text: enabled: false suggested_questions: - "What can you help me with?" suggested_questions_after_answer: enabled: false text_to_speech: enabled: false graph: edges: - id: "1711536487001-source-1711536522001-target" source: "1711536487001" sourceHandle: source target: "1711536522001" targetHandle: target type: custom zIndex: 0 data: sourceType: start targetType: llm - id: "1711536522001-source-1711536558001-target" source: "1711536522001" sourceHandle: source target: "1711536558001" targetHandle: target type: custom zIndex: 0 data: sourceType: llm targetType: answer nodes: - id: "1711536487001" type: custom position: { x: 80 , y: 282 } data: type: start title: Start desc: "" variables: [] - id: "1711536522001" type: custom position: { x: 380 , y: 282 } data: type: llm title: LLM desc: "" model: provider: "langgenius/openai/openai" name: "gpt-4o-mini" mode: "chat" completion_params: temperature: 0.7 prompt_template: - role: "system" text: "You are a helpful assistant." variables: [] context: enabled: false variable_selector: [] vision: enabled: false memory: query_prompt_template: " {{#sys.query#}} " window: enabled: false size: 10