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/comfyui-node-datatypes

ComfyUI data types - IMAGE, LATENT, MASK, CONDITIONING, MODEL, CLIP, VAE, AUDIO, VIDEO, 3D types, widget types, and custom types. Use when working with ComfyUI tensors, model types, or defining input/output data types.

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comfyui-custom-node-skills
2659 skills
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$ npx -y skills add jtydhr88/comfyui-custom-node-skills --skill comfyui-node-datatypes --agent claude-code

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How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/comfyui-node-datatypes

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ComfyUI data types - IMAGE, LATENT, MASK, CONDITIONING, MODEL, CLIP, VAE, AUDIO, VIDEO, 3D types, widget types, and custom types. Use when working with ComfyUI tensors, model types, or defining input/output data types.

SKILL.md

comfyui-node-datatypes.SKILL.md
name: comfyui-node-datatypes
description: ComfyUI data types - IMAGE, LATENT, MASK, CONDITIONING, MODEL, CLIP, VAE, AUDIO, VIDEO, 3D types, widget types, and custom types. Use when working with ComfyUI tensors, model types, or defining input/output data types.

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 |

3D Types

| Type | V3 Class | Python Type | Description | |---|---|---|---| | MESH | `io.Mesh` | `MESH(vertices, faces)` | 3D mesh with vertices + faces tensors | | VOXEL | `io.Voxel` | `VOXEL(data)` | Voxel data tensor | | SPLAT | `io.Splat` | `SPLAT` | Gaussian splat data | | FILE_3D | `io.File3DAny` | `File3D` | Any supported 3D format | | FILE_3D_GLB | `io.File3DGLB` | `File3D` | Binary glTF | | FILE_3D_GLTF | `io.File3DGLTF` | `File3D` | JSON-based glTF | | FILE_3D_FBX | `io.File3DFBX` | `File3D` | FBX format | | FILE_3D_OBJ | `io.File3DOBJ` | `File3D` | OBJ format | | FILE_3D_STL | `io.File3DSTL` | `File3D` | STL format (3D printing) | | FILE_3D_USDZ | `io.File3DUSDZ` | `File3D` | Apple AR format | | FILE_3D_PLY | `io.File3DPLY` | `File3D` | PLY (point cloud / splat) | | FILE_3D_SPLAT | `io.File3DSPLAT` | `File3D` | .splat gaussian splat file | | FILE_3D_SPZ | `io.File3DSPZ` | `File3D` | Compressed splat (.spz) | | FILE_3D_KSPLAT | `io.File3DKSPLAT` | `File3D` | .ksplat format | | FILE_3D_SPLAT_ANY | `io.File3DSplatAny` | `File3D` | Any splat format | | FILE_3D_POINT_CLOUD_ANY | `io.File3DPointCloudAny` | `File3D` | Any point cloud format | | SVG | `io.SVG` | `SVG` | Scalable vector graphics | | LOAD_3D | `io.Load3D` | `Model3DDict` (see below) | 3D model with renders | | LOAD_3D_ANIMATION | `io.Load3DAnimation` | Same as Load3D | Animated 3D model | | LOAD3D_CAMERA | `io.Load3DCamera` | `CameraInfo` (see below) | 3D camera info | | LOAD3D_MODEL_INFO | `io.Load3DModelInfo` | `list[Model3DTransform]` | Per-model transforms (position/quaternion/scale) |

**Load3D.Model3DDict**: `{"image": str, "mask": str, "normal": str, "camera_info": CameraInfo, "recording"?: str, "model_3d_info"?: list[Model3DTransform]}`

**Load3DCamera.CameraInfo** (right-handed, Y-up, camera looks down -Z): required keys `position`, `target`, `zoom`, `cameraType` (`'perspective' | 'orthographic'`); optional keys `quaternion` (camera world rotation), `fov` (vertical, degrees, perspective only), `aspect`, `near`, `far`, `frustum` (orthographic only: `{left, right, top, bottom}`).

**Load3DModelInfo.Model3DTransform**: `{"position": dict, "quaternion": dict, "scale": dict}` in world space.

Widget Types (create UI controls)

| Type | V3 Class | Python Type | Description | |---|---|---|---| | INT | `io.Int` | `int` | Integer with min/max/step | | FLOAT | `io.Float` | `float` | Float with min/max/step/round | | STRING | `io.String` | `str` | Text (single/multi-line) | | BOOLEAN | `io.Boolean` | `bool` | Toggle with labels | | COMBO | `io.Combo` | `str` | Dropdown selection | | COMBO (multi) | `io.MultiCombo` | `list[str]` | Multi-select dropdown | | COLOR | `io.Color` | `str` (hex) | Color picker, default `#ffffff` | | COLORS | `io.Colors` | `list[str]` (hex) | Color palette (list of colors) | | BOUNDING_BOX | `io.BoundingBox` | `{"x": int, "y": int, "width": int

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Ships withcomfyui-custom-node-skills

A curated collection of agent skills (for Claude Code and OpenAI Codex) for developing ComfyUI custom nodes. These skills give the agent comprehensive knowledge of the ComfyUI node system, covering both the V3 (recommended) and V1 (legacy) APIs.

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