opencreator-skills
OpenCreator Workflow Skill
Activation
Use this skill when the task involves any of:
- Searching or running OpenCreator templates
- Running an existing workflow and getting results
- Building or editing a workflow graph (nodes + edges)
- UGC, storyboard video, ecommerce multi-image, or similar content creation
Mode Decision
User request
│
├─ Run template / get results / "帮我做 XX" ──► Operate Mode (default)
│
└─ Create workflow / edit graph / "从零搭" / no suitable template ──► Build Mode
Always try Operate Mode first. Switch to Build Mode only when:
- No suitable template exists after searching
- The user explicitly asks to create or edit a workflow
- The required graph differs materially from any available template
If a task needs both, do Build first (produce the graph), then Operate (run it).
Operate Mode
Must read: references/api-workflows.md
This single file covers the complete Operate flow:
- Configuration (Base URL, API Key)
- Search templates by keyword
- Present candidates, user selects
- Copy template → get
flow_id - Query runtime parameters
- Collect user inputs (ask every field, never use defaults)
- Run workflow
- Poll status
- Deliver results (media directly, not just links)
Supplementary (read only when you need deeper tactics):
references/best-practices.md— template-first strategy and design principles
Operate Hard Rules
- Always copy template before running (public templates are read-only)
- Always query parameters before each run (node IDs can change)
inputsmust be flat:{ "node_id": "value" }— never wrap in extra object- Never expose
node_id/inputText/imageBase64to users — use business language - Search results must be ranked by relevance and only show top 5 to the user
- After starting a run, you MUST poll until terminal state (success/failed/cancelled) — never stop and wait for the user to ask. This is your #1 obligation.
- On success, immediately fetch results and deliver media to the user — do not end your turn without delivering.
- Poll every 10 s for text/image, 30 s for video
- Deliver media directly, not just URLs
Build Mode
When building or editing a workflow graph, follow these four steps in order. Do not skip any step.
Step 1: Structure Reverse-Planning
Work backward from the user's final deliverable to identify the abstract structure and module dependencies.
Answer these questions first:
- What is the final output?
- Does it need a semantic layer (text/script generation)?
- Does it need a visual branch (image/video)?
- Does it need an audio branch (TTS/music)?
- Does it need a compositing layer?
- Can all leaf inputs trace back to user input or generatable primitives?
Must read:
references/step-1-reverse-plan/workflow-reverse-planner.mdreferences/node-catalog.md
Output: Macro Format + Dependency Graph
Step 2: Generator Selection & Wiring
Map abstract modules to concrete generators and plan edges + naming.
Must read:
references/step-1-reverse-plan/generator-wiring-naming-planner.mdreferences/step-1-reverse-plan/generator-routing.md
Then read the matching file in references/step-2-generators/ (see routing table below).
Step 3: Model Selection & Parameters
Choose models, fill selectedModels and parameters for each node.
Hard rule before choosing any model:
- Treat the
Confirmed model IDstables in each Step 3 file as the source of truth for model IDs - Only use model IDs that are explicitly mapped to the current node type / atom
- If a model is recommended in prose but its exact ID is not listed for that atom, do not use it
- Never translate a marketing name (for example
Sora 2,GPT Image 1.5,Seedream 5.0 Lite) into a guessed model ID - If an atom has no dedicated Step 3 file, use
references/node-catalog.mdas the fallback source of truth - If an atom still has no stable model-selection entry after checking those docs, keep the documented fixed behavior and do not invent
selectedModels
Read the matching file in references/step-3-models/ (see routing table below).
Step 4: Prompt Writing
Write prompts for nodes that need inputText.
Must read:
references/step-4-prompts/prompt-prewrite-reasoner.md
Then read the matching prompt best-practices file (see routing table below).
Step 2 Generator Routing Table
Text Generation
- Text only → script:
references/step-2-generators/reference-text-generator.md - Reference image → text:
references/step-2-generators/reference-image-text-generator.md - Reference video → text:
references/step-2-generators/reference-video-text-generator.md - Multimodal input → text:
references/step-2-generators/multimodal-text-generator.md - Script → storyboard split:
references/step-2-generators/storyboard-text-splitter.md
Image Generation
- Text → image:
references/step-2-generators/text-to-image-generator.md - Multi-image reference → image:
references/step-2-generators/image-reference-generator.md - Storyboard batch images:
references/step-2-generators/storyboard-image-generator.md - Relight:
references/step-2-generators/relight-image-generator.md - Angle control:
references/step-2-generators/angle-control-image-generator.md
Video Generation
- Text → video:
references/step-2-generators/text-to-video-generator.md - Image → video:
references/step-2-generators/image-to-video-generator.md - Storyboard broadcast:
references/step-2-generators/storyboard-video-generator.md - Storyboard aligned:
references/step-2-generators/storyboard-video-generator-aligned.md - Omni video:
references/step-2-generators/omni-video-generator.md - Lipsync:
references/step-2-generators/lipsync-video-generator.md - Motion transfer:
references/step-2-generators/motion-transfer-video-generator.md - Video modify / style transfer:
references/step-2-generators/video-modify-generator.md
Audio Generation
- Text → speech:
references/step-2-generators/text-to-speech-generator.md - Voice cloning:
references/step-2-generators/voice-cloning-generator.md - Music:
references/step-2-generators/music-generator.md
Step 3 Model Routing Table
- Source of truth for exact IDs: the
Confirmed model IDstable in each file below, withreferences/node-catalog.mdas fallback for nodes without a dedicated Step 3 file textGenerator/scriptSplit:references/step-3-models/text-generator-model-selection.mdimageMaker:references/step-3-models/text-to-image-model-selection.mdimageToImage:references/step-3-models/image-to-image-model-selection.mdvideoMaker:references/step-3-models/image-to-video-model-selection.mdtextToVideo:references/step-3-models/text-to-video-model-selection.mdtextToSpeech:references/step-3-models/text-to-speech-model-selection.md- Input nodes:
references/step-3-models/input-block-skill.md - Not listed above: check
references/node-catalog.md
Step 4 Prompt Routing Table
- Before writing any prompt:
references/step-4-prompts/prompt-prewrite-reasoner.md textGeneratorprompts:references/step-4-prompts/text-prompt-best-practices.md- Image node prompts:
references/step-4-prompts/image-prompt-best-practices.md - Video node prompts:
references/step-4-prompts/video-prompt-best-practices.md
Key Concepts
Broadcast
1 image + N texts → N results. Must use imageInput as the reference image source, not a generated image.
Alignment
N images + N texts, 1:1 pairing. Counts must match exactly.
List Propagation
scriptSplit outputs a text list; downstream generators auto-expand per item — do not duplicate generator nodes.
Shared Semantic Layer
In complex scenarios (lipsync ads, multi-branch video), generate a shared structured brief first, then fork to visual and audio branches.
Scenario References
- UGC lipsync ad:
references/scenarios/scenario-ugc-lipsync-ad.md - Storyboard video:
references/scenarios/scenario-storyboard-video.md - Ecommerce multi-image:
references/scenarios/scenario-ecommerce-multi-image.md
Build Mode Output
After completing the four steps, output standard nodes + edges JSON.
Node and edge schema: references/node-catalog.md
Save via create_workflow tool if available, otherwise via the Workflow PATCH API (see references/api-workflows.md §10).