What does the Comfyui Node Inputs AI skill do?

ComfyUI node input types - INT, FLOAT, STRING, BOOLEAN, COMBO widgets, hidden inputs, optional inputs, lazy inputs, force_input. Use when configuring node inputs, adding widgets, or customizing input behavior.

Why use Comfyui Node Inputs on TypingMind?

Because you install it once and use it with any model. Comfyui Node Inputs is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Comfyui Node Inputs in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jtydhr88/comfyui-custom-node-skills/tree/master/plugins/comfyui-custom-nodes/skills/comfyui-node-inputs. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Comfyui Node Inputs?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Comfyui Node Inputs?

As many as you like. As long as a model supports skills, you can use Comfyui Node Inputs with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Comfyui Node Inputs AI skill free?

Yes. It is published on GitHub by jtydhr88 under the MIT license. You only pay your own AI provider for the tokens you use.

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

Comfyui Node Inputs by jtydhr88 — AI Skill for Any LLM

ComfyUI Node Inputs

Inputs define what data a node accepts. Widget inputs create UI controls; connection inputs create socket slots.

Widget Input Types

INT

python
io.Int.Input("seed",
    default=0,
    min=0,
    max=0xffffffffffffffff,
    step=1,
    control_after_generate=True,  # adds increment/decrement/randomize control
    display_mode=io.NumberDisplay.number,  # "number", "slider", or "gradient_slider"
    tooltip="Random seed for generation",
)

NumberDisplay options: io.NumberDisplay.number, io.NumberDisplay.slider, io.NumberDisplay.gradient_slider

ControlAfterGenerate options: True (default randomize), or io.ControlAfterGenerate.fixed, .increment, .decrement, .randomize. A mode value is treated as the control's starting mode only from frontend v1.57.0 (#19867); older frontends used the string as the control widget's name.

FLOAT

python
io.Float.Input("strength",
    default=1.0,
    min=0.0,
    max=10.0,
    step=0.01,
    round=0.001,         # rounding precision
    display_mode=io.NumberDisplay.slider,
    gradient_stops=[{"offset": 0.0, "color": [0, 0, 0]}, {"offset": 1.0, "color": [255, 255, 255]}],  # for gradient_slider mode
    tooltip="Effect strength",
)

STRING

python
# Single-line string
io.String.Input("name",
    default="",
    placeholder="Enter name...",
)

# Multi-line text area
io.String.Input("prompt",
    multiline=True,
    default="",
    placeholder="Enter prompt...",
    dynamic_prompts=True,  # enable dynamic prompt syntax
)

BOOLEAN

python
io.Boolean.Input("enabled",
    default=True,
    label_on="Enabled",
    label_off="Disabled",
    tooltip="Toggle this feature",
)

COMBO (Dropdown)

python
io.Combo.Input("mode",
    options=["option_a", "option_b", "option_c"],
    default="option_a",
    tooltip="Select processing mode",
    control_after_generate=True,  # adds increment/decrement/randomize control
)

Combo with Enum:

python
from enum import Enum

class BlendMode(Enum):
    NORMAL = "normal"
    MULTIPLY = "multiply"
    SCREEN = "screen"

io.Combo.Input("blend", options=BlendMode, default=BlendMode.NORMAL)
# Enum values auto-converted to string list

Combo with file upload:

python
io.Combo.Input("image_file",
    options=[],
    upload=io.UploadType.image,          # .image, .audio, .video, .model (for generic file upload)
    image_folder=io.FolderType.input,    # .input, .output, .temp
)

Dynamic combo with remote options:

python
io.Combo.Input("model_name",
    options=[],
    remote=io.RemoteOptions(
        route="/internal/models/checkpoints",
        refresh_button=True,
        control_after_refresh="first",  # "first" or "last"
        timeout=5000,        # ms
        max_retries=3,
        refresh=60000,       # TTL refresh interval in ms
    ),
)

Security: treat combo / DynamicCombo values as untrusted when they reach the filesystem. Re-validate any value used as a file/folder name, path, format or extension at the load/save boundary (folder_paths.get_full_path_or_raise(...), folder_paths.is_within_directory(base, path), or a fixed allowlist) — prompts can be submitted via the API with values outside options.

MULTICOMBO (Multi-select Dropdown)

python
io.MultiCombo.Input("tags",
    options=["tag1", "tag2", "tag3", "tag4"],
    default=["tag1"],
    placeholder="Select tags...",
    chip=True,  # display as chips
)
# Value type: list[str]

COLOR (Color Picker)

python
io.Color.Input("color",
    default="#ffffff",
    socketless=True,  # widget only by default
)
# Value type: str (hex color)

COLORS (Color Palette)

python
io.Colors.Input("palette",
    default=["#ff0000", "#00ff00"],
    socketless=True,
)
# Value type: list[str] (hex colors)

BOUNDING_BOX (Rectangle Selector)

python
io.BoundingBox.Input("region",
    default={"x": 0, "y": 0, "width": 512, "height": 512},
    socketless=True,
    component="my_component",  # optional custom UI component name
    force_input=False,
)
# Value type: {"x": int, "y": int, "width": int, "height": int}

BOUNDING_BOXES (Multiple Regions)

python
io.BoundingBoxes.Input("regions",
    default=[],
    socketless=True,
)
# Value type: list of {"x": int, "y": int, "width": int, "height": int, "metadata": dict}

CURVE (Spline Editor)

python
io.Curve.Input("curve",
    default=[(0.0, 0.0), (1.0, 1.0)],  # linear ramp
    socketless=True,
)
# Value type: raw curve data; normalize with CurveInput.from_raw(value)
# (from comfy_api.input import CurveInput)

RANGE (Levels/Range Editor)

python
io.Range.Input("levels",
    default={"min": 0.0, "max": 1.0},
    gradient_stops=None,     # gradient background for the slider
    show_midpoint=True,      # gamma midpoint handle
    value_min=0.0,
    value_max=1.0,
)
# Value type: raw dict; normalize with RangeInput.from_raw(value)
# (from comfy_api.input import RangeInput) -> .min_val, .max_val, .midpoint, .to_lut()

WEBCAM (Camera Capture)

python
io.Webcam.Input("capture")
# Value type: str

IMAGECOMPARE (Comparison Widget)

python
io.ImageCompare.Input("comparison", socketless=True)
# Value type: dict

Newer editor widgets (v0.31–v0.40, need a matching frontend)

python
io.VideoEdit.Input("trim", features=["trim"])   # "trim" and/or "crop" (default both)
# Value: {"trim": {"start_time": float, "duration": float}, "crop": {"x","y","width","height"}}
#   apply with video.as_trimmed(...) / video.as_cropped(...); zero width/height = full frame
io.ResolutionPreview.Input("preview",            # live preview of a computed size; optional by default
    ratio_widget="aspect_ratio", megapixels_widget="megapixels", multiple_widget="multiple")
io.Compositor.Input("compositor")                # layer-editor state dict (pairs with io.Layers)
io.CameraInfoState.Input("camera_info_state", optional=True)  # camera editor state dict

Input Options (Common to All)

python
io.Image.Input("image",
    optional=True,        # not required; creates optional input socket
    tooltip="Description shown on hover",
    lazy=True,            # lazy evaluation - only computed when needed
    advanced=True,        # hidden by default in compact mode
    raw_link=True,        # receive raw link reference instead of value
)

force_input

Forces a widget input to appear as a connection socket instead of a widget:

python
io.Float.Input("value",
    default=1.0,
    force_input=True,   # shows as socket, not slider
)

socketless

Makes a widget input appear only as a widget with no input socket:

python
io.String.Input("note",
    default="",
    socketless=True,     # widget only, no connection socket
)

Optional Inputs

python
class MyNode(io.ComfyNode):
    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="MyNode",
            display_name="My Node",
            category="example",
            inputs=[
                io.Image.Input("image"),                        # required
                io.Mask.Input("mask", optional=True),           # optional
                io.Float.Input("blend", default=0.5),           # has default widget
            ],
            outputs=[io.Image.Output("IMAGE")],
        )

    @classmethod
    def execute(cls, image, mask=None, blend=0.5):
        # Optional inputs default to None when not connected
        if mask is not None:
            image = image * (1 - blend) + image * mask.unsqueeze(-1) * blend
        return io.NodeOutput(image)

Hidden Inputs

Hidden inputs receive server-provided values, not user input:

python
class MyNode(io.ComfyNode):
    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="MyNode",
            display_name="My Node",
            category="example",
            inputs=[io.String.Input("text")],
            outputs=[io.String.Output()],
            hidden=[
                io.Hidden.unique_id,       # node's unique ID
                io.Hidden.prompt,           # full prompt data
                io.Hidden.extra_pnginfo,    # PNG metadata dict
                io.Hidden.dynprompt,        # dynamic prompt object
                io.Hidden.auth_token_comfy_org,  # auth token
                io.Hidden.api_key_comfy_org,     # API key
                io.Hidden.comfy_usage_source,    # prompt source, e.g. "comfyui-frontend"
                # io.Hidden.execution_list exists (v0.36+) but is core-internal and
                # slated for removal - DO NOT USE
            ],
        )

    @classmethod
    def execute(cls, text):
        # Access hidden values via cls.hidden
        node_id = cls.hidden.unique_id
        prompt = cls.hidden.prompt
        extra = cls.hidden.extra_pnginfo
        return io.NodeOutput(f"{text} (node: {node_id})")

Lazy Evaluation

Lazy inputs are only evaluated when actually needed, saving computation:

python
class ConditionalNode(io.ComfyNode):
    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="ConditionalNode",
            display_name="Conditional",
            category="logic",
            inputs=[
                io.Boolean.Input("condition"),
                io.Image.Input("if_true", lazy=True),
                io.Image.Input("if_false", lazy=True),
            ],
            outputs=[io.Image.Output("IMAGE")],
        )

    @classmethod
    def check_lazy_status(cls, condition, if_true=None, if_false=None):
        """Return list of input names that need evaluation."""
        if condition and if_true is None:
            return ["if_true"]
        if not condition and if_false is None:
            return ["if_false"]
        return []

    @classmethod
    def execute(cls, condition, if_true, if_false):
        return io.NodeOutput(if_true if condition else if_false)

Rules for lazy evaluation:

Mark inputs with lazy=True
Implement check_lazy_status() classmethod
Unevaluated inputs are None; a disconnected optional lazy input is absent from kwargs — use a sentinel default (MISSING = object()) to distinguish (see the Switch pattern in comfyui-node-advanced)
Return list of input names that need computing, or empty list
Method may be called multiple times
Lazy sub-inputs of Autogrow/DynamicCombo arrive as (value, key) tuples here (see comfyui-node-advanced)

Input tolerance (core policy): don't reject values just because they exceed recommended/UI limits or may give poor results; adapt (clamp/pad/resize) only when the operation would otherwise fail. Security checks (path containment) still apply.

V1 Input Format (Legacy Reference)

python
@classmethod
def INPUT_TYPES(s):
    return {
        "required": {
            "image": ("IMAGE",),
            "strength": ("FLOAT", {
                "default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01
            }),
            "mode": (["option_a", "option_b"],),
            "text": ("STRING", {"multiline": True, "default": ""}),
        },
        "optional": {
            "mask": ("MASK",),
        },
        "hidden": {
            "unique_id": "UNIQUE_ID",
            "prompt": "PROMPT",
            "extra_pnginfo": "EXTRA_PNGINFO",
        },
    }

Complete Example: Multi-Input Node

python
class AdvancedImageNode(io.ComfyNode):
    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="AdvancedImageNode",
            display_name="Advanced Image",
            category="image/advanced",
            description="Demonstrates various input types",
            inputs=[
                # Required connection input
                io.Image.Input("image", tooltip="Input image"),
                # Required widget inputs
                io.Float.Input("brightness", default=1.0, min=0.0, max=3.0,
                               step=0.1, display_mode=io.NumberDisplay.slider),
                io.Float.Input("contrast", default=1.0, min=0.0, max=3.0, step=0.1),
                io.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff,
                             control_after_generate=True),
                io.Combo.Input("blend_mode", options=["normal", "multiply", "screen"]),
                io.Boolean.Input("flip_horizontal", default=False),
                io.String.Input("label", default="", socketless=True),
                # Optional inputs
                io.Mask.Input("mask", optional=True),
                io.Image.Input("overlay", optional=True),
                # Advanced inputs (collapsed by default)
                io.Float.Input("gamma", default=1.0, min=0.1, max=3.0, advanced=True),
            ],
            outputs=[
                io.Image.Output("IMAGE"),
                io.Mask.Output("MASK"),
            ],
        )

    @classmethod
    def execute(cls, image, brightness, contrast, seed, blend_mode,
                flip_horizontal, label, mask=None, overlay=None, gamma=1.0):
        result = image * brightness
        if flip_horizontal:
            result = torch.flip(result, dims=[2])
        if mask is not None:
            result = result * mask.unsqueeze(-1)
        return io.NodeOutput(result, mask if mask is not None else torch.ones(result.shape[:3]))

Publisher	jtydhr88
Repository	`comfyui-custom-node-skills`
Skill name	`comfyui-node-inputs`
Stars	306
Forks	28
Bundled files	Instructions only
License	MIT
Links	Skill source Repository

Comfyui Node Inputs

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

ComfyUI Node Inputs

Widget Input Types

INT

FLOAT

STRING

BOOLEAN

COMBO (Dropdown)

MULTICOMBO (Multi-select Dropdown)

COLOR (Color Picker)

COLORS (Color Palette)

BOUNDING_BOX (Rectangle Selector)

BOUNDING_BOXES (Multiple Regions)

CURVE (Spline Editor)

RANGE (Levels/Range Editor)

WEBCAM (Camera Capture)

IMAGECOMPARE (Comparison Widget)

Newer editor widgets (v0.31–v0.40, need a matching frontend)

Input Options (Common to All)

force_input

socketless

Optional Inputs

Hidden Inputs

Lazy Evaluation

V1 Input Format (Legacy Reference)

Complete Example: Multi-Input Node

See Also

Frequently asked questions

Set up your own AI workspace now

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

ComfyUI Node Inputs

Widget Input Types

INT

FLOAT

STRING

BOOLEAN

COMBO (Dropdown)

MULTICOMBO (Multi-select Dropdown)

COLOR (Color Picker)

COLORS (Color Palette)

BOUNDING_BOX (Rectangle Selector)

BOUNDING_BOXES (Multiple Regions)

CURVE (Spline Editor)

RANGE (Levels/Range Editor)

WEBCAM (Camera Capture)

IMAGECOMPARE (Comparison Widget)

Newer editor widgets (v0.31–v0.40, need a matching frontend)

Input Options (Common to All)

force_input

socketless

Optional Inputs

Hidden Inputs

Lazy Evaluation

V1 Input Format (Legacy Reference)

Complete Example: Multi-Input Node

See Also

Frequently asked questions

Related AI Skills

Set up your own AI workspace now