---
name: comfyui
description: Run local ComfyUI workflows via the HTTP API. Use when the user asks to run ComfyUI, execute a workflow by file path/name, or supply raw API-format JSON; supports the default workflow bundled in assets.
read_when:
  - User asks to generate images with ComfyUI
  - User provides a workflow file or JSON to run
  - User describes an image to generate (subject, style, scene)
  - User pastes or sends a list of model weight URLs to download for ComfyUI
metadata: {"clawdbot":{"emoji":"🖼️","requires":{"bins":["python3"]}}}
---

# ComfyUI Runner

## Overview
Run ComfyUI workflows on the local server (default 127.0.0.1:8188) using API-format JSON and return output images.

## Editing the workflow before running
The run script only takes `--workflow <path>`. You must **inspect and edit the workflow JSON** before running, using your best knowledge of the ComfyUI API format. Do not assume fixed node IDs, `class_type` names, or `_meta.title` values — the user may have updated the default workflow or supplied a custom one.

**For every run (including the default workflow):**
1. Read the workflow JSON (default: `skills/comfyui/assets/default-workflow.json`, or the path/file the user gave).
2. **Identify prompt-related nodes** by inspecting the graph: look for nodes that hold the main text prompt — e.g. `PrimitiveStringMultiline`, `CLIPTextEncode` (positive text), or any node with `_meta.title` or `class_type` suggesting "Prompt" / "positive" / "text". Update the corresponding input (e.g. `inputs.value`, or the text input to the encoder) to the image prompt you derived from the user (subject, style, lighting, quality). If the user didn’t ask for a custom image, you can leave the existing prompt or tweak only if needed.
3. **Optionally identify style/prefix nodes** — e.g. `StringConcatenate`, or a second string input that acts as style. Set them if the user asked for a specific style or to clear a default prefix.
4. **Optionally set a new seed** — find sampler-like nodes (e.g. `KSampler`, `BasicGuider`, or any node with a `seed` input) and set `seed` to a new random integer so each run can differ.
5. Write the modified workflow to a temp file (e.g. `skills/comfyui/assets/tmp-workflow.json`). Use `~/ComfyUI/venv/bin/python` for any inline Python; do not use bare `python`.
6. Run: `comfyui_run.py --workflow <path-to-edited-json>`.

If the workflow structure is unclear or you can’t find prompt/sampler nodes, run the file as-is and only change what you can reliably identify. Same approach for arbitrary user-supplied JSON: inspect first, edit at your best knowledge, then run.

## Run script (single responsibility)
```bash
~/ComfyUI/venv/bin/python skills/comfyui/scripts/comfyui_run.py \
  --workflow <path-to-workflow.json>
```

The script only queues the workflow and polls until done. It prints JSON with `prompt_id` and output `images`. All prompt/style/seed changes are done by you in the JSON beforehand.

## If the server isn’t reachable
If the run script fails with a connection error (e.g. connection refused or timeout to 127.0.0.1:8188), ComfyUI may not be installed or not running.

**Check:** Does `~/ComfyUI` exist and contain `main.py`?

- **If not installed:** Install ComfyUI (e.g. clone the repo, create a venv, install dependencies, then start the server). Example:
  ```bash
  git clone https://github.com/comfyanonymous/ComfyUI.git ~/ComfyUI
  cd ~/ComfyUI
  python3 -m venv venv
  ~/ComfyUI/venv/bin/pip install -r requirements.txt
  ```
  Then start the server (see below). Tell the user they may need to install model weights into `~/ComfyUI/models/` depending on the workflow.

- **If installed but not running:** Start the ComfyUI server so the API is available on port 8188. Example:
  ```bash
  ~/ComfyUI/venv/bin/python ~/ComfyUI/main.py --listen 127.0.0.1
  ```
  Run in the background or in a separate terminal so it keeps running. Then retry the workflow run.

Use `~` (or the user’s home) for paths so it works on their machine.

## Model weights from URLs
When the user pastes or sends a **list of model weight URLs** (one per line, or comma-separated), download those files into the ComfyUI installation so the workflow can use them later.

1. **Normalize the list** — one URL per line; strip empty lines and comments (lines starting with `#`).
2. **Run the download script** with the ComfyUI base path (default `~/ComfyUI`). The script uses [pget](https://github.com/replicate/pget) for parallel downloads when available; if `pget` is not in PATH, it installs it to `~/.local/bin` automatically (no sudo). If pget cannot be installed (e.g. unsupported OS/arch), it falls back to a built-in download. Use the ComfyUI venv Python so the script runs correctly:
   ```bash
   ~/ComfyUI/venv/bin/python skills/comfyui/scripts/download_weights.py --base ~/ComfyUI
   ```
   Pass URLs as arguments, or pipe a file/list on stdin:
   ```bash
   echo "https://example.com/model.safetensors" | ~/ComfyUI/venv/bin/python skills/comfyui/scripts/download_weights.py --base ~/ComfyUI
   ```
   Or save the user’s list to a temp file and run:
   ```bash
   ~/ComfyUI/venv/bin/python skills/comfyui/scripts/download_weights.py --base ~/ComfyUI < /tmp/weight_urls.txt
   ```
   To force the built-in download (no pget): add `--no-pget`.
3. **Subfolder:** The script infers the ComfyUI models subfolder from the URL/filename (e.g. `vae`, `clip`, `loras`, `checkpoints`, `text_encoders`, `controlnet`, `upscale_models`). The user can optionally specify a subfolder per line as `url subfolder` (e.g. `https://.../model.safetensors vae`). You can also pass a default with `--subfolder loras` so all URLs in that run go to `models/loras/`.
4. **Existing files:** By default the script skips URLs that already exist on disk; use `--overwrite` to replace.
5. **Paths:** Files are written under `~/ComfyUI/models/<subfolder>/`. Tell the user where each file was saved and that they can run the workflow once the ComfyUI server is (re)started if needed.

Supported subfolders (under `ComfyUI/models/`): `checkpoints`, `clip`, `clip_vision`, `controlnet`, `diffusion_models`, `embeddings`, `loras`, `text_encoders`, `unet`, `vae`, `vae_approx`, `upscale_models`, and others. Use `--subfolder <name>` when the auto-inference is wrong.

## After run
Outputs are saved under `ComfyUI/output/`. Use the `images` list from the script output to locate the files (filename + subfolder).

### ⚠️ Always send the output to the user
After a successful ComfyUI run, **you must deliver the generated image(s) to the user**. Do not reply with only the filename in text or with NO_REPLY.

1. Parse the script output JSON for `images` (each has `filename`, `subfolder`, `type`).
2. Build the full path: `ComfyUI/output/` + subfolder + filename (e.g. `ComfyUI/output/z-image_00007_.png`).
3. **Send the image to the user** via the channel they're on (e.g. use the message/send tool with the image `path` so the user receives the file). Include a short caption if helpful (e.g. "Here you go." or "Tokyo street scene.").

Every successful run must result in the user receiving the image. Never leave them with only a filename or no delivery.

## Resources

### scripts/
- `comfyui_run.py`: Queue a workflow, poll until completion, print `prompt_id` and `images`. No args — you edit the JSON before running.
- `download_weights.py`: Download model weight URLs into `~/ComfyUI/models/<subfolder>/`. Uses [pget](https://github.com/replicate/pget) when available (installs to `~/.local/bin` if missing); fallback to built-in download. Input: URLs as args or one per line on stdin. Options: `--base`, `--subfolder`, `--overwrite`, `--no-pget`. Infers subfolder from URL/filename when not given.

### assets/
- `default-workflow.json`: Default workflow. Copy and edit (prompt, style, seed) then run with the edited path; or run as-is for a generic run.


---
Source attribution: kbarbel640-del
Original project: https://github.com/kbarbel640-del/skills/blob/e7e336ac628c3e3bd771aff1aaa469c93778f94a/skills/kelvincai522/comfyui/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 clawdbot

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: kbarbel640-del
Original project: https://github.com/kbarbel640-del/skills/blob/e7e336ac628c3e3bd771aff1aaa469c93778f94a/skills/kelvincai522/comfyui/SKILL.md
Attribution basis: Source repository record

Fields of work: Design, Marketing
Roles: Content Marketer, Graphic Designer, UX Designer
Specialties: API
Tools: Comfyui
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: f46b7dbfe375fe09cb0630a036dce8a02c703b1e99ee30906b0d6b614683dc1e
