Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/sandyup/comfyui-mcp/troubleshootingnpx skills add sandyup/comfyui-mcp --skill troubleshootinggit clone --depth 1 https://github.com/sandyup/comfyui-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/sandyup/comfyui-mcp/troubleshooting)<a href="https://agentmods.dev/skills/sandyup/comfyui-mcp/troubleshooting"><img src="https://agentmods.dev/badge/skills/sandyup/comfyui-mcp/troubleshooting.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00028 | $0.04795 |
| Opus 5 | $0.00014 | $0.02397 |
| Sonnet 5 | $0.00006 | $0.00959 |
| Haiku 4.5 | $0.00003 | $0.00479 |
Grade A, and why
troubleshooting scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 493 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ComfyUI Troubleshooting Guide
Render completes but looks WRONG (artifacts, wrong subject/pose/color, a ControlNet/mask/LoRA not taking, a refiner degrading it)? That's not an error — use the debug-render skill (
read_skill("debug-render")): localize the bad stage with run-to-node (panel_runto_node_id) by previewing intermediate steps. This guide is for runs that fail with an error/OOM/missing node.
Error Diagnosis Strategy
When a workflow fails, follow this systematic approach:
- Get the error: Use
get_historyto retrieve the execution result with full traceback - Check logs: Use
get_logswith keyword filters like"error","warning","traceback" - Identify the failing node: The history response includes the
node_idandnode_typethat failed - Cross-reference inputs: Use
get_node_infoto verify the failing node's expected input schema - Check models: Use
list_local_modelsto verify all referenced model files exist
Out of Memory (OOM)
Error Pattern
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate X MiB.
GPU 0 has a total capacity of 24.00 GiB of which X MiB is free.
Or:
RuntimeError: CUDA error: out of memory
Root Cause
The GPU does not have enough VRAM to hold the model weights, intermediate tensors, and latent images simultaneously. Common triggers:
- High resolution images (2048x2048+)
- Multiple models loaded simultaneously
- FP32 precision models on limited VRAM
- Video generation (LTXV, AnimateDiff) with many frames
- Large batch sizes
Fixes (in order of preference)
- Reduce resolution: Drop to the model's native resolution (512 for SD 1.5, 1024 for SDXL/Flux)
- Use FP8/FP16 quantized models: FP8 Flux models use ~8GB vs ~24GB for FP16
- Search for FP8 variants:
search_models("flux fp8")orsearch_models("sdxl fp8")
- Search for FP8 variants:
- Launch flags (the VRAM ladder): offload aggressively via ComfyUI CLI flags —
--lowvram— offload text encoders / model parts to CPU--novram— extreme offload; the go-to for long video (LTX 2 / WAN) OOM--cache-none— cache nothing (lowest RAM/VRAM); combine with--novram--reserve-vram N— reserve N GB so the GPU stops spilling into slow shared VRAM (Windows); typical2–4--disable-smart-memory— force offload to RAM when a run gets stuck / intermittently OOMs- Full matrix + recipes:
comfyui-launch-flags
- Free VRAM between generations: ComfyUI should auto-manage, but restarting clears leaked memory
- Use tiled VAE decoding: For high-resolution images, tile the VAE decode step
- Node:
VAEDecodeTiledinstead ofVAEDecode - Breaks the image into tiles, decodes each separately, and stitches them together
- Node:
- Reduce batch size: Set batch_size to 1 in
EmptyLatentImage - Avoid multiple models: Don't load two full checkpoints simultaneously — use one checkpoint and LoRAs instead
- For LTXV/video: Always use FP8 quantized video models on 24GB cards
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 493 lines · 28 tokens per session scan A d70c4b4aefbd
troubleshooting is a skill published in the GitHub repository sandyup/comfyui-mcp (1 stars, last pushed 1mo ago), licensed MIT. It adds 28 tokens to every session and 4,795 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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