Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add jtydhr88/comfyui-custom-node-skills --skill comfyui-node-datatypesgit clone --depth 1 https://github.com/jtydhr88/comfyui-custom-node-skillsWrote 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/jtydhr88/comfyui-custom-node-skills/comfyui-node-datatypes)<a href="https://agentmods.dev/skills/jtydhr88/comfyui-custom-node-skills/comfyui-node-datatypes"><img src="https://agentmods.dev/badge/skills/jtydhr88/comfyui-custom-node-skills/comfyui-node-datatypes.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00067 | $0.05273 |
| Opus 5 | $0.00034 | $0.02636 |
| Sonnet 5 | $0.00013 | $0.01055 |
| Haiku 4.5 | $0.00007 | $0.00527 |
Grade A, and why
comfyui-node-datatypes 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 8d 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 — 487 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ComfyUI Data Types
ComfyUI uses specific data types for node inputs and outputs. Understanding tensor shapes and data formats is essential.
Complete Type Reference
Tensor/Data Types
| Type | V3 Class | Format | Description |
|---|---|---|---|
| IMAGE | io.Image |
torch.Tensor [B,H,W,C] float32 0-1 |
Batch of RGB images |
| MASK | io.Mask |
torch.Tensor [H,W] or [B,H,W] float32 0-1 |
Grayscale masks |
| LATENT | io.Latent |
{"samples": Tensor[B,C,H,W] or [B,C,T,H,W], "noise_mask"?: Tensor, "batch_index"?: list[int], "type"?: str} |
Latent space (4D image / 5D video) |
| CONDITIONING | io.Conditioning |
list[tuple[Tensor, PooledDict]] |
Text conditioning with pooled outputs |
| AUDIO | io.Audio |
{"waveform": Tensor[B,C,T], "sample_rate": int} |
Audio data |
| VIDEO | io.Video |
VideoInput ABC |
Video data (abstract base class) |
| SIGMAS | io.Sigmas |
torch.Tensor 1D, length steps+1 |
Noise schedule |
| NOISE | io.Noise |
Object with generate_noise() |
Noise generator |
| LORA_MODEL | io.LoraModel |
dict[str, torch.Tensor] |
LoRA weight deltas |
| LOSS_MAP | io.LossMap |
{"loss": list[torch.Tensor]} |
Loss map |
| TRACKS | io.Tracks |
{"track_path": Tensor, "track_visibility": Tensor} |
Motion tracking data |
| WAN_CAMERA_EMBEDDING | io.WanCameraEmbedding |
torch.Tensor |
WAN camera embeddings |
| LATENT_OPERATION | io.LatentOperation |
Callable[[Tensor], Tensor] |
Latent transform function |
| TIMESTEPS_RANGE | io.TimestepsRange |
tuple[int, int] |
Range 0.0-1.0 |
| DICT | io.Dict |
dict |
Generic dictionary |
| ARRAY | io.Array |
list |
Generic list/array |
Model Types (opaque, typically pass-through)
| Type | V3 Class | Python Type |
|---|---|---|
| MODEL | io.Model |
ModelPatcher |
| CLIP | io.Clip |
CLIP |
| VAE | io.Vae |
VAE |
| CONTROL_NET | io.ControlNet |
ControlNet |
| CLIP_VISION | io.ClipVision |
ClipVisionModel |
| CLIP_VISION_OUTPUT | io.ClipVisionOutput |
ClipVisionOutput |
| STYLE_MODEL | io.StyleModel |
StyleModel |
| GLIGEN | io.Gligen |
ModelPatcher (wrapping Gligen) |
| UPSCALE_MODEL | io.UpscaleModel |
ImageModelDescriptor |
| BACKGROUND_REMOVAL | io.BackgroundRemoval |
BackgroundRemovalModel (e.g. BiRefNet) |
| LATENT_UPSCALE_MODEL | io.LatentUpscaleModel |
Any |
| SAMPLER | io.Sampler |
Sampler |
| GUIDER | io.Guider |
CFGGuider |
| HOOKS | io.Hooks |
HookGroup |
| HOOK_KEYFRAMES | io.HookKeyframes |
HookKeyframeGroup |
| MODEL_PATCH | io.ModelPatch |
Any |
| AUDIO_ENCODER | io.AudioEncoder |
Any |
| AUDIO_ENCODER_OUTPUT | io.AudioEncoderOutput |
Any |
| PHOTOMAKER | io.Photomaker |
Any |
| POINT | io.Point |
Any |
| FACE_ANALYSIS | io.FaceAnalysis |
Any |
| BBOX | io.BBOX |
Any |
| SEGS | io.SEGS |
Any |
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.
- 8d ago First seen · 487 lines · 67 tokens per session scan A 8ba4212ff5c8
comfyui-node-datatypes is a skill published in the GitHub repository jtydhr88/comfyui-custom-node-skills (277 stars, last pushed 1mo ago), licensed MIT. It adds 67 tokens to every session and 5,273 once invoked, about $0.0003 per session on Opus 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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