How do I install the Comfy Nodes skill?

Run npx skillmds@latest add tomevault-io/comfy-nodes in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Comfy Nodes safe to use?

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Which AI agents work with Comfy Nodes?

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Is Comfy Nodes free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Comfy Nodes?

tomevault-io (@tomevault-io) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Comfy Nodes by tomevault-io — Agent Skill

Name: Comfy Nodes
Author: tomevault-io

File contents

ComfyUI Custom Node Development

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

Quick Template

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

# 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:

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

Converted and distributed by TomeVault — claim your Tome and manage your conversions.

---name: comfy-nodesdescription: 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.---# ComfyUI Custom Node DevelopmentThis skill helps you create custom ComfyUI nodes from Python code.## Quick Template```pythonclass 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 NodeWhen you have Python code to wrap:### Step 1: Identify inputs and outputs```python# Original functiondef 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 dimensionComfyUI images are `[B,H,W,C]` - always process all batch items:```pythondef 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](references/NODE_TEMPLATE.md) - Full template with V3 schema- [OFFICIAL_DOCS.md](references/OFFICIAL_DOCS.md) - Official ComfyUI documentation- [PURZ_EXAMPLES.md](references/PURZ_EXAMPLES.md) - Example nodes and workflows## Finding Similar NodesUse the MCP tools to find existing nodes for reference:```comfy_search("blur") → Find blur implementationscomfy_spec("GaussianBlur") → See how inputs are defined```---> Converted and distributed by [TomeVault](https://tomevault.io/claim/peteromallet) — claim your Tome and manage your conversions.

Frequently asked questions

Comfy Nodes