---

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)

~/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:

  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:

  ~/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 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:

   ~/ComfyUI/venv/bin/python skills/comfyui/scripts/download_weights.py --base ~/ComfyUI
   

   Pass URLs as arguments, or pipe a file/list on stdin:

   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:

   ~/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 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.