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 anima-lora-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/anima-lora-trainer)<a href="https://agentmods.dev/skills/sandyup/comfyui-mcp/anima-lora-trainer"><img src="https://agentmods.dev/badge/skills/sandyup/comfyui-mcp/anima-lora-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 | $0.00064 | $0.02544 |
| Opus 5 | $0.00032 | $0.01272 |
| Sonnet 5 | $0.00013 | $0.00509 |
| Haiku 4.5 | $0.00006 | $0.00254 |
Grade A, and why
anima-lora-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 8d 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.
`app.py` runs Gradio on **`0.0.0.0:7860`** → open **http://127.0.0.1:7860**. Re-launch later with `run_anima_base_windows.bat` (Win) or `./run_anima_base_runpod.sh` (RunPod). The DiT base model **auto-downloads on first This is a copy
91% identical to anima-lora-trainer — 55 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Citron Anima LoRA Trainer
Overview
Citron's Anima LoRA Trainer (app.py = "🍋 Citron's Anima LoRA Trainer") is a local Gradio UI for training LoRA adapters on the Anima diffusion model using kohya-ss/sd-scripts. It trains on ~6GB VRAM with the default settings — same low-VRAM profile as Anima generation.
- Created by Citron Legacy; UI repo:
https://github.com/citronlegacy/citron-anima-lora-trainer-ui. The Aitrepreneur adaptive installers clone the forkhttps://github.com/aitrepreneur/citron-anima-lora-trainer-ui. - Training backend:
kohya-ss/sd-scripts(https://github.com/kohya-ss/sd-scripts), launched viaaccelerate launch. - Trains LoRAs for Anima DiT (Cosmos-2B). Uses Anima's own components: DiT weights + Qwen3-0.6B text encoder + Qwen-Image VAE.
- Output: standard
.safetensorsLoRA usable directly in the anima-base ComfyUI workflow.
Network module is
networks.lora_animaand the training script issd-scripts/anima_train_network.py(an Anima-specific kohya script the installer expects). Confirm these exist after the installer'sgit cloneof sd-scripts — they are referenced byapp.pybut pulled from the upstream repo at install time.
Setup
Windows
Run CITRON_ANIMA_LORA_TRAINER-V2.bat. It:
- Ensures Git and Python 3.10 (via winget if missing).
- Detects the NVIDIA GPU/driver and picks a matching PyTorch CUDA wheel automatically:
- Blackwell (RTX 50xx) → cu128, bf16
- Modern (RTX 20/30/40, etc.) → cu128/cu126/cu118 by driver, bf16 (fp16 on Turing)
- Pascal/Maxwell (GTX 10/9xx) → cu126/cu118, fp16
- Kepler/older → unsupported
- Clones the UI repo, patches
app.pydefaults (base_model→anima-preview3-base,mixed_precision→ detected value), writesapp_configs/accelerate_gpu.yaml. - Creates
.venv, installs PyTorch, clones+installssd-scripts, installs apprequirements.txt. - Downloads models into
models/anima/{dit,text_encoder,vae}/fromhttps://huggingface.co/circlestone-labs/Anima/resolve/main/split_files/...:dit/anima-base-v1.0.safetensors(~4GB)text_encoder/qwen_3_06b_base.safetensors(~1.19GB)vae/qwen_image_vae.safetensors(~254MB)
- Writes and launches
run_anima_base_windows.bat.
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.
- 8d ago First seen · 163 lines · 64 tokens per session scan A 7ff5928b780a
anima-lora-trainer is a skill published in the GitHub repository sandyup/comfyui-mcp (1 stars, last pushed 2mo ago), licensed MIT. It adds 64 tokens to every session and 2,544 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 91% identical to anima-lora-trainer, differing in 55 lines, and is treated as a copy.
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Generates images and text via reverse-engineered Gemini Web API. Supports text generation, image generation from prompts, reference images for vision input, and multi-turn conversations. Use when other skills need image generation backend, or when user requests "generate image with Gemini", "Gemini text generation"…
seedance-antislop
This skill should be used when a Seedance 2.0 prompt contains generic AI filler, hollow superlatives, vague cinematic language, bloated adjectives, weak verbs, or needs sharper production-specific wording.
seedance-filter
This skill should be used when a Seedance 2.0 prompt is blocked or rejected, when moderation is a suspected cause of a problem, or when the user asks for a content-boundary review or safer alternative. Assess the actual request before offering a clarification.
seedance-vocab-en
This skill should be used when an English Seedance 2.0 prompt needs clearer production wording, less generic prose, or precise vocabulary for camera, lighting, motion, VFX, audio, and constraints. Route blocked prompts through seedance-filter for context and boundary review.