Agent skill

comfy-nodes

Use when the user wants to create a ComfyUI custom node, convert Python code to a node, make a node from a script, or needs help with ComfyUI node development, INPUT_TYPES, RETURN_TYPES, or node class structure.

View SKILL.md on GitHub Repository
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Install this agent skill to your Project

npx add-skill https://github.com/ConstantineB6/comfy-pilot/tree/main/.claude/skills/comfy-nodes

SKILL.md

ComfyUI Custom Node Development

This skill helps you create custom ComfyUI nodes from Python code.

Quick Template

python
class MyNode:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "image": ("IMAGE",),
                "value": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0}),
            },
            "optional": {
                "mask": ("MASK",),
            }
        }

    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES = ("output",)
    FUNCTION = "execute"
    CATEGORY = "Custom/MyNodes"

    def execute(self, image, value, mask=None):
        result = image * value
        return (result,)

NODE_CLASS_MAPPINGS = {"MyNode": MyNode}
NODE_DISPLAY_NAME_MAPPINGS = {"MyNode": "My Node"}

Converting Python to Node

When you have Python code to wrap:

Step 1: Identify inputs and outputs

python
# Original function
def apply_blur(image, radius=5):
    from PIL import ImageFilter
    return image.filter(ImageFilter.GaussianBlur(radius))

Step 2: Map types

Python Type ComfyUI Type Conversion
PIL Image IMAGE torch.from_numpy(np.array(pil) / 255.0)
numpy array IMAGE torch.from_numpy(arr.astype(np.float32))
cv2 BGR IMAGE torch.from_numpy(cv2.cvtColor(img, cv2.COLOR_BGR2RGB) / 255.0)
float 0-255 IMAGE Divide by 255.0
Single image Batch tensor.unsqueeze(0)

Step 3: Handle batch dimension

ComfyUI images are [B,H,W,C] - always process all batch items:

python
def execute(self, image, radius):
    batch_results = []
    for i in range(image.shape[0]):
        # Convert to PIL
        img_np = (image[i].cpu().numpy() * 255).astype(np.uint8)
        pil_img = Image.fromarray(img_np)

        # Your processing
        result = pil_img.filter(ImageFilter.GaussianBlur(radius))

        # Convert back
        result_np = np.array(result).astype(np.float32) / 255.0
        batch_results.append(torch.from_numpy(result_np))

    return (torch.stack(batch_results),)

Common Input Types

Type Shape/Format Widget Options
IMAGE [B,H,W,C] float 0-1 -
MASK [H,W] or [B,H,W] float 0-1 -
LATENT {"samples": [B,C,H,W]} -
MODEL ModelPatcher -
CLIP CLIP encoder -
VAE VAE model -
CONDITIONING [(cond, pooled), ...] -
INT integer default, min, max, step
FLOAT float default, min, max, step, display
STRING str default, multiline
BOOLEAN bool default
COMBO str List of options as type

Checklist

  • INPUT_TYPES is a @classmethod
  • Return value is a tuple: return (result,)
  • Handle batch dimension [B,H,W,C]
  • Add to NODE_CLASS_MAPPINGS
  • Category uses / for submenus

References

  • NODE_TEMPLATE.md - Full template with V3 schema
  • OFFICIAL_DOCS.md - Official ComfyUI documentation
  • PURZ_EXAMPLES.md - Example nodes and workflows

Finding Similar Nodes

Use the MCP tools to find existing nodes for reference:

comfy_search("blur")        → Find blur implementations
comfy_spec("GaussianBlur")  → See how inputs are defined

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