Borrowing it
Nothing to install: this file belongs to ConstantineB6/comfy-pilot. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ConstantineB6/comfy-pilot/main/.claude/skills/comfy-nodes/SKILL.mdgit clone --depth 1 https://github.com/ConstantineB6/comfy-pilotWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/constantineb6/comfy-pilot/comfy-nodes)<a href="https://agentmods.dev/skills/constantineb6/comfy-pilot/comfy-nodes"><img src="https://agentmods.dev/badge/skills/constantineb6/comfy-pilot/comfy-nodes/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/constantineb6/comfy-pilot/comfy-nodes"><img src="https://agentmods.dev/badge/skills/constantineb6/comfy-pilot/comfy-nodes.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00054 | $0.00964 |
| Opus 5.5 | $0.00022 | $0.00386 |
| Sonnet 5.5 | $0.00011 | $0.00193 |
| Haiku 4.5 | $0.00005 | $0.00096 |
Grade A, and why
comfy-nodes scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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),)
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 123 lines · 54 tokens per session scan A f63482c70115
comfy-nodes is a skill published in the GitHub repository ConstantineB6/comfy-pilot (233 stars, last pushed 8mo ago), licensed MIT. It adds 54 tokens to every session and 964 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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