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Debug Render

Community
artokun
debug-render

Debug a WRONG or imperfect render (not a hard error) by inspecting inputs and intermediate steps with run-to-node. Render one branch up to an output, preview-tap latents/masks/preprocessor maps, localize the first bad stage, then fix. Use when a final image/video completes but looks wrong, such as artifacts, wrong subject/pose/composition/color, blur, a ControlNet/IPAdapter/mask/LoRA not taking, or a two-stage refiner or upscale degrading the result. For errors/OOM/missing nodes use the troubleshooting skill.

Overview

Publisherartokun
Repositorycomfyui-mcp
Skill namedebug-render
Stars
811
Forks
137
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by artokun on GitHub. Read the source before you install it.

Installation

Install the Debug Render AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/artokun/comfyui-mcp/tree/main/plugin/skills/debug-render
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/artokun/comfyui-mcp.git /tmp/comfyui-mcp
mkdir -p .claude/skills
cp -r /tmp/comfyui-mcp/plugin/skills/debug-render .claude/skills/debug-render
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Debug Render in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Debug Render on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Debug Render is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Debugging renders with run-to-node

When a final asset looks wrong (not when it errors), don't re-roll the whole graph and hope. Localize the fault. Render only as far as one stage and look at what that stage actually produces. The wrong-looking output is downstream of a first bad step. Find that step, fix it there, and everything after it follows.

The tool for this is panel_run with to_node_id ("run to node"), which uses ComfyUI's native partial execution. Only the target output node plus everything upstream of it renders; every other branch is skipped. That makes each probe fast and cheap, and the output is delivered to you automatically, same as any run. Don't poll.

When to reach for this

  • A final image/video is wrong: artifacts, wrong subject/pose/composition, off color, soft/blurry, melted hands, wrong style.
  • A conditioner isn't "taking" (ControlNet, IPAdapter, a mask, an inpaint, a LoRA) and you can't tell if the input to it is wrong or the node is wrong.
  • A multi-stage pipeline (base → refiner, txt2img → upscale, decode → post) degrades, and you need to see which stage introduces the problem.
  • You only want to test one branch of a multi-output graph without paying for the others.

If instead the run fails (red error, OOM, missing node, black image from a crash), use the troubleshooting skill. This skill is for outputs that complete but look wrong.

The one hard rule: to_node_id must be an OUTPUT node

ComfyUI can only run to an output node: SaveImage, PreviewImage, SaveVideo, SaveAudio, etc. (these show is_output: true in panel_query_graph detail rows). A bare KSampler / VAEDecode / preprocessor is not an output node, so you can't target it directly. To inspect a point that isn't already an output, add a preview tap there:

You want to see…Wire typeTap to addWiring
An intermediate image (post-process, refiner stage, composite)IMAGEPreviewImageimage → PreviewImage.images
A latent (after a sampler, before/after upscale)LATENTVAEDecode → PreviewImagelatent → VAEDecode.samples; reuse the graph's VAE → VAEDecode.vae; then → PreviewImage
A mask (inpaint, segmentation, attention)MASKMaskToImage → PreviewImagemask → MaskToImage.mask → PreviewImage
A ControlNet/preprocessor map (depth/pose/canny)usually IMAGE alreadyPreviewImagethe preprocessor's IMAGE output → PreviewImage.images
The actual prompt/conditioningCONDITIONING(can't preview as an image)inspect widget values / the text node feeding it instead

Build a tap with panel_add_node + panel_connect, run to it, inspect, then panel_remove_node the tap when you're done (unless the user wants to keep it).

The loop

  1. Read the graph. panel_graph_outline (then panel_query_graph for specifics). Note node ids, the output nodes (is_output: true), and every node's mode. A node in bypass/mute on the path is OFF and is a top cause of wrong renders. Fix modes first with panel_set_node_mode before you blame anything else.
  2. Pick a probe point roughly in the middle of the suspect chain (bisect).
  3. Get an output there. Target an existing output node, or add a preview tap (table above).
  4. panel_run(to_node_id = that output node). Only that branch renders.
  5. Look at what's delivered. Is this stage already wrong, or still fine?
    • Still fine: move the probe downstream; the fault is later.
    • Already wrong: move the probe upstream; the fault is earlier.
  6. Repeat until you find the first stage whose output is bad. That node (or its inputs/widgets) is what to fix, via panel_set_widget, panel_set_node_mode, a rewire, or a different model. Then run-to-node there again to confirm the fix before running the full graph.
  7. Clean up temporary preview taps (panel_remove_node) and do a final full panel_run to produce the real saved asset.

Symptom → where to probe first

  • Whole image wrong subject/style. Preview the conditioning's source (the text/prompt node, the sampler's positive input via a decode of its latent).
  • ControlNet ignored / wrong structure. Preview the preprocessor map (the depth/pose/canny IMAGE going into the ControlNet). A blank or wrong map means the problem is the preprocessor or its input, not the sampler.
  • IPAdapter/reference not taking. Preview the reference image after any resize/crop the IPAdapter chain applies.
  • Inpaint/outpaint bleeding. MaskToImage-preview the mask actually reaching the sampler (mask misalignment is the usual culprit).
  • Refiner/upscale degrades it. Preview the base latent (VAEDecode tap) before the refiner vs after. If the base is good and the result is bad, the refiner/upscale stage is at fault.
  • Right content, bad quality only. The fault is late (sampler steps/cfg, VAE, post), so probe near the end first.

Gotchas

  • Output-node only. If you target a non-output node, the run is rejected with a message saying so. Add a preview tap instead.
  • Modes matter. A bypassed/muted node on the probed path silently changes what renders. Check modes in step 1.
  • Auto-delivery. The probe's output node fires an executed event and the image is delivered to you inline, including PreviewImage taps, which arrive labeled as a preview (temporary, not a saved file). Don't poll; just end the turn and read the delivered image. A preview tap with no SaveImage means "no saved output ran", which is expected for a probe.
  • Shared queue. Each run-to-node queues a real (small) job. Don't stack probes. Run one, read it, then the next.
  • Subgraphs. Output nodes nested inside a subgraph can't be targeted yet. Probe at the root graph (add the tap outside the subgraph, fed by an exposed output rail).

Sources

  • Official: comfyui-mcp panel_run to_node_id product behavior (this repo).
  • Empirical: probe-point recipes from observed wrong-render cases.

Frequently asked questions

What does the Debug Render AI skill do?

Debug a WRONG or imperfect render (not a hard error) by inspecting inputs and intermediate steps with run-to-node. Render one branch up to an output, preview-tap latents/masks/preprocessor maps, localize the first bad stage, then fix. Use when a final image/video completes but looks wrong, such as artifacts, wrong subject/pose/composition/color, blur, a ControlNet/IPAdapter/mask/LoRA not taking, or a two-stage refiner or upscale degrading the result. For errors/OOM/missing nodes use the troubleshooting skill.

Why use Debug Render on TypingMind?

Because you install it once and use it with any model. Debug Render 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 Debug Render in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/artokun/comfyui-mcp/tree/main/plugin/skills/debug-render. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Debug Render?

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 Debug Render?

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

Is the Debug Render AI skill free?

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

What are AI skills?

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.

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