comfyui-workflow-builder
Translates natural language requests into executable ComfyUI workflow JSON. Always validates against inventory before generating.
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Install Skill
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Installation Guide
How to use comfyui-workflow-builder on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your machine
- ›Node.js 16+ with npm — verify with
node --version - ›Active project directory where you want to add
comfyui-workflow-builder
Run the install command
Execute the skills CLI command in your project's root directory to begin installation:
Fetches comfyui-workflow-builder from mckruz/comfyui-expert and configures it for Cursor.
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate comfyui-workflow-builder. Access via /comfyui-workflow-builder in your agent's command palette.
Security 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 environment. Always review source, verify the publisher, and test in isolation before production.
Documentation
ComfyUI Workflow Builder
Translates natural language requests into executable ComfyUI workflow JSON. Always validates against inventory before generating.
Workflow Generation Process
Step 1: Understand the Request
Parse the user's intent into:
- Output type: Image, video, or audio
- Source material: Text-only, reference image(s), existing video
- Identity method: None, zero-shot (InstantID/PuLID), LoRA, Kontext
- Quality level: Draft (fast iteration) vs production (maximum quality)
- Special requirements: ControlNet, inpainting, upscaling, lip-sync
Step 2: Check Inventory
Read state/inventory.json to determine:
- Available checkpoints → select best match for task
- Available identity models → determine which methods are possible
- Available ControlNet models → enable pose/depth control if available
- Custom nodes installed → verify all required nodes exist
- VRAM available → optimize settings accordingly
Step 3: Select Pipeline Pattern
Based on request + inventory, choose from:
| Pattern | When | Key Nodes |
|---|---|---|
| Text-to-Image | Simple generation | Checkpoint → CLIP → KSampler → VAE |
| Identity-Preserved Image | Character consistency | + InstantID/PuLID/IP-Adapter |
| LoRA Character | Trained character | + LoRA Loader |
| Image-to-Video (Wan) | High-quality video | Diffusion Model → Wan I2V → Video Combine |
| Image-to-Video (AnimateDiff) | Fast video, motion control | + AnimateDiff Loader + Motion LoRAs |
| Talking Head | Character speaks | Image → Video → Voice → Lip-Sync |
| Upscale | Enhance resolution | Image → UltimateSDUpscale → Save |
| Inpainting | Edit regions | Image + Mask → Inpaint Model → KSampler |
Step 4: Generate Workflow JSON
ComfyUI workflow format:
{
"{node_id}": {
"class_type": "{NodeClassName}",
"inputs": {
"{param_name}": "{value}",
"{connected_param}": ["{source_node_id}", {output_index}]
}
}
}
Rules:
- Node IDs are strings (typically "1", "2", "3"...)
- Connected inputs use array format:
["source_node_id", output_index] - Output index is 0-based integer
- Filenames must match exactly what's in inventory
- Seed values: use random large integer or fixed for reproducibility
Step 5: Validate
Before presenting to user:
- Every
class_typeexists in inventory's node list - Every model filename exists in inventory's model list
- All required connections are present (no dangling inputs)
- VRAM estimate doesn't exceed available VRAM
- Resolution is compatible with chosen model (512 for SD1.5, 1024 for SDXL/FLUX)
Step 6: Output
If online mode: Queue via comfyui-api skill
If offline mode: Save JSON to projects/{project}/workflows/ with descriptive name
Workflow Templates
Basic Text-to-Image (FLUX)
{
"1": {
"class_type": "LoadCheckpoint",
"inputs": {"ckpt_name": "flux1-dev.safetensors"}
},
"2": {
"class_type": "CLIPTextEncode",
"inputs": {"text": "{positive_prompt}", "clip": ["1", 1]}
},
"3": {
"class_type": "CLIPTextEncode",
"inputs": {"text": "{negative_prompt}", "clip": ["1", 1]}
},
"4": {
"class_type": "EmptyLatentImage",
"inputs": {"width": 1024, "height": 1024, "batch_size": 1}
},
"5": {
"class_type": "KSampler",
"inputs": {
"seed": 42,
"steps": 25,
"cfg": 3.5,
"sampler_name": "euler",
"scheduler": "normal",
"denoise": 1.0,
"model": ["1", 0],
"positive": ["2", 0],
"negative": ["3", 0],
"latent_image": ["4", 0]
}
},
"6": {
"class_type": "VAEDecode",
"inputs": {"samples": ["5", 0], "vae": ["1", 2]}
},
"7": {
"class_type": "SaveImage",
"inputs": {"filename_prefix": "output", "images": ["6", 0]}
}
}
With Identity Preservation (InstantID + IP-Adapter)
Extends basic template by adding:
- Load reference image node
- InstantID Model Loader + Apply InstantID
- IPAdapter Unified Loader + Apply IPAdapter
- FaceDetailer post-processing
See references/workflows.md for complete node settings.
Video Generation (Wan I2V)
Uses different loader chain:
- Load Diffusion Model (not LoadCheckpoint)
- Wan I2V Conditioning
- EmptySD3LatentImage (with frame count)
- Video Combine (VHS)
See references/workflows.md Workflow 4 for complete settings.
VRAM Estimation
| Component | Approximate VRAM |
|---|---|
| FLUX FP16 | 16GB |
| FLUX FP8 | 8GB |
| SDXL | 6GB |
| SD1.5 | 4GB |
| InstantID | +4GB |
| IP-Adapter | +2GB |
| ControlNet (each) | +1.5GB |
| Wan 14B | 20GB |
| Wan 1.3B | 5GB |
| AnimateDiff | +3GB |
| FaceDetailer | +2GB |
Common Mistakes to Avoid
- Wrong output index: CheckpointLoader outputs
[model, clip, vae]at indices[0, 1, 2] - CFG too high for InstantID: Use 4-5, not default 7-8
- Wrong resolution for model: FLUX/SDXL=1024, SD1.5=512
- Missing VAE: FLUX needs explicit VAE (
ae.safetensors) - Wrong model in wrong loader: Diffusion models need
LoadDiffusionModel, notLoadCheckpoint
Reference Files
references/workflows.md- Detailed node-by-node templatesreferences/models.md- Model files and pathsreferences/prompt-templates.md- Model-specific promptsstate/inventory.json- Current inventory cache
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Use Cases
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Steps
- 1Install skill using provided installation command
- 2Test with simple use case relevant to your work
- 3Evaluate output quality and relevance
- 4Iterate on prompts to improve results
- 5Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
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Reviews
- LLuis Shah★★★★★Dec 24, 2024
comfyui-workflow-builder is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- CCarlos Johnson★★★★★Dec 20, 2024
Keeps context tight: comfyui-workflow-builder is the kind of skill you can hand to a new teammate without a long onboarding doc.
- KKofi Jackson★★★★★Dec 20, 2024
We added comfyui-workflow-builder from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- YYuki Gill★★★★★Dec 20, 2024
Keeps context tight: comfyui-workflow-builder is the kind of skill you can hand to a new teammate without a long onboarding doc.
- CChaitanya Patil★★★★★Dec 16, 2024
Keeps context tight: comfyui-workflow-builder is the kind of skill you can hand to a new teammate without a long onboarding doc.
- JJin Gupta★★★★★Nov 15, 2024
Solid pick for teams standardizing on skills: comfyui-workflow-builder is focused, and the summary matches what you get after install.
- DDev Martin★★★★★Nov 11, 2024
comfyui-workflow-builder has been reliable in day-to-day use. Documentation quality is above average for community skills.
- KKofi Kim★★★★★Nov 11, 2024
comfyui-workflow-builder has been reliable in day-to-day use. Documentation quality is above average for community skills.
- PPiyush G★★★★★Nov 7, 2024
comfyui-workflow-builder has been reliable in day-to-day use. Documentation quality is above average for community skills.
- SShikha Mishra★★★★★Oct 26, 2024
Solid pick for teams standardizing on skills: comfyui-workflow-builder is focused, and the summary matches what you get after install.
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