comfyui-workflow-builder

Q: How do I install comfyui-workflow-builder?

Run npx skills add https://github.com/mckruz/comfyui-expert --skill comfyui-workflow-builder in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/comfyui-workflow-builder/ (personal) or .claude/skills/comfyui-workflow-builder/ (project).

by mckruzProductivity & Automation

Generate, build, create, or design ComfyUI workflow JSON from natural language descriptions. Produces valid node graphs with correct classtypes, connections, output indices, and model-appropriate settings. Handles txt2img, img2img, inpainting, ControlNet, LoRA stacking, upscaling, and face detailing pipelines. Does NOT cover ComfyUI installation, custom node development, Python scripting, model training, hardware advice, or architectural explanations.

Installs: 2.1K
Last 24h: +9
Trending rank: #9365
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/mckruz/comfyui-expert --skill comfyui-workflow-builder

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads comfyui-workflow-builder automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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_type exists 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, not LoadCheckpoint

Reference Files

references/workflows.md - Detailed node-by-node templates
references/models.md - Model files and paths
references/prompt-templates.md - Model-specific prompts
state/inventory.json - Current inventory cache

FAQ

What does the comfyui-workflow-builder skill do?

How do I install comfyui-workflow-builder?

Run npx skills add https://github.com/mckruz/comfyui-expert --skill comfyui-workflow-builder in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/comfyui-workflow-builder/ (personal) or .claude/skills/comfyui-workflow-builder/ (project).

Which AI agents can use comfyui-workflow-builder?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is comfyui-workflow-builder safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains comfyui-workflow-builder?

It is published by mckruz in mckruz/comfyui-expert. It has 2,095 recorded installs.

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comfyui-workflow-builder

Install

Install

Describe the task

Review the result

SKILL.md

ComfyUI Workflow Builder

Workflow Generation Process

Step 1: Understand the Request

Step 2: Check Inventory

Step 3: Select Pipeline Pattern

Step 4: Generate Workflow JSON

Step 5: Validate

Step 6: Output

Workflow Templates

Basic Text-to-Image (FLUX)

With Identity Preservation (InstantID + IP-Adapter)

Video Generation (Wan I2V)

VRAM Estimation

Common Mistakes to Avoid

Reference Files

FAQ

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