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 skills add sandyup/comfyui-mcp --skill ai-toolkit-trainergit 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/ai-toolkit-trainer)<a href="https://agentmods.dev/skills/sandyup/comfyui-mcp/ai-toolkit-trainer"><img src="https://agentmods.dev/badge/skills/sandyup/comfyui-mcp/ai-toolkit-trainer.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 5.1 | $0.00093 | $0.03193 |
| Opus 5 | $0.00046 | $0.01597 |
| Sonnet 5 | $0.00019 | $0.00639 |
| Haiku 4.5 | $0.00009 | $0.00319 |
Grade A, and why
ai-toolkit-trainer scanned grade A with 1 finding 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 7d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- **V2 — `AI-TOOLKIT_AUTO_INSTALL-V2.bat`** (recommended): uses an **embedded Python 3.10.11**, **auto-installs Git + Node**, builds a clean PATH without your system Python, and adds aggressive pip/curl retries — far few How it starts
The opening of the file, as written. The whole thing — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI-Toolkit LoRA Trainer (WAN 2.2 & Z-Image)
Overview
AI-Toolkit by ostris is "the ultimate training toolkit for finetuning diffusion models" (MIT license) — a standalone trainer with its own web UI, NOT a ComfyUI custom node. It runs a Node.js UI front end over a Python (run.py) training backend, and trains LoRAs for many model families — here we cover WAN 2.2 / 2.1 video models and Z-Image (Turbo & Base).
- Repo:
https://github.com/ostris/ai-toolkit(cloned by the installers). - Backend:
python run.py config/<job>.yml. UI: a Node.js app underui/that schedules/monitors jobs (you don't have to keep the UI open while a job runs). - Output: a standard
.safetensorsLoRA you drop into ComfyUImodels/loras/and load withLoraLoaderModelOnly.
Best for:
- WAN LoRAs — a person/character, an art style, or a specific camera/video motion (image or video clip datasets). For using WAN see wan-t2v-video / wan-flf-video.
- Z-Image LoRAs — fast, very low-VRAM image LoRAs (faces, characters, outfits, styles) on the 6B Z-Image base/turbo. For using Z-Image see z-image-base / z-image-turbo (and the z-image-xy-plot pack to compare trained LoRAs).
For low-VRAM anime image LoRAs on a different stack (kohya sd-scripts), see the sibling anima-lora-trainer.
Two LoRA kinds (WAN): a WAN image LoRA trains on still images (cheaper, ~24GB-class, good for identity/style); a WAN video LoRA trains on short clips (heavier — best on cloud — good for motion). Z-Image is image-only.
Install
The installer comes in two generations — both clone ostris/ai-toolkit, set up Torch for your GPU, and launch the web UI. Put it in a folder whose full path has NO spaces (e.g. C:\AI-Toolkit).
- V1 —
AI-TOOLKIT_AUTO_INSTALL.bat: expects Git, Python 3.10.x, and Node 18+ already in PATH. - V2 —
AI-TOOLKIT_AUTO_INSTALL-V2.bat(recommended): uses an embedded Python 3.10.11, auto-installs Git + Node, builds a clean PATH without your system Python, and adds aggressive pip/curl retries — far fewer prerequisites and the more robust choice. (Used for the Z-Image Turbo LoRA training release.)
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.
- 7d ago First seen · 146 lines · 93 tokens per session scan A 6905ad592d9a
ai-toolkit-trainer is a skill published in the GitHub repository sandyup/comfyui-mcp (1 stars, last pushed 2mo ago), licensed MIT. It adds 93 tokens to every session and 3,193 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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