Discover Civitai models with the BUILT-IN search_civitai_models tool and install/generate them locally — find a checkpoint/LoRA/embedding on Civitai, download it into ComfyUI, and use its trigger words. Optionally pair the official Civitai MCP for community features (images browsing, posting, collections).
npx skills add https://github.com/artokun/comfyui-mcp --skill civitai
comfyui-mcp has native Civitai search built in — search_civitai_models —
plus the local half of the loop: download, wire, queue, tag. The full flow
(find → install → generate) needs no other server, no API key, and works on
EVERY backend, including small local models behind the compact router.
search_civitai_models({ query, types: ["LORA"], base_models: ["Flux.1 D"] })
│ each hit: model_id · model_version_id · trigger words
▼
download_civitai_model({ model_version_id, target_subfolder: "loras" })
│
▼
list_local_models → panel_add_node loader / generate_image # use it
base_models when the user's checkpoint family is known —a Flux LoRA will not load on an SDXL checkpoint (see model-compatibility).
CivitAI labels: Flux.1 D, SDXL 1.0, SD 1.5, Pony, Illustrious,
Wan Video.
target_subfolder must match the model type: checkpoints, loras, vae,controlnet, embeddings, upscale_models, …
nsfw: true to opt in).model_version_id over model_id — a Civitai page can list severalversions and the user usually means a specific one.
API key (optional). Search needs none. CIVITAI_API_TOKEN (from
civitai.com/user/account) unlocks gated/early-access downloads and gated
search results — set it once (panel Settings › "Set CivitAI token…" or env).
"Find me a good anime LoRA for Flux and install it"
search_civitai_models({ query: "anime style", types: ["LORA"], base_models: ["Flux.1 D"] }).version id. Let the user pick.
download_civitai_model({ model_version_id, target_subfolder: "loras" }).panel_add_node a LoraLoader, panel_set_widget thelora_name, wire it between checkpoint and sampler — and use the hit's
trigger words in the prompt. Headless: generate_image / build the workflow.
"Download this Civitai page for me" (user pastes a URL)
modelVersionId from the URL if present; otherwise pass the modelid from the URL to download_civitai_model (it resolves the latest version).
A raw civitai.com/api/download/... URL also works via download_model
with CIVITAI_API_TOKEN set.
For features beyond search→install — browsing example images and their
generation params, collections, posting, reviews, bounties — pair the official
remote server (no longer auto-bundled; add it once):
claude mcp add --transport http civitai https://mcp.civitai.com/mcp \
--header "Authorization: Bearer YOUR_CIVITAI_API_KEY"
Then mcp__civitai__* tools appear alongside comfyui-mcp's. Its discovery
results hand off identically (modelVersions[].id → download_civitai_model).
Boundaries: that server also exposes write/social tools (post,
comment, review, DM, follow). Those publish on the user's behalf — surface
what you're about to post and get an explicit yes first. This skill is about
discovery → local install → generation; don't post or message without being
asked.
model-registry — curated direct download URLs (HF + Civitai notes)model-compatibility — base-model / VAE / CLIP pairing (why a LoRA won't load)prompt-engineering — Civitai image params are a prompt goldmineLightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.
Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.
This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.
This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.
Digital pathology image processing toolkit for whole slide images (WSI). Use this skill when working with histopathology slides, processing H&E or IHC stained tissue images, extracting tiles from gigapixel pathology images, detecting tissue regions, segmenting tissue masks, or preparing datasets for computational pathology deep learning pipelines. Applies to WSI formats (SVS, TIFF, NDPI), tile-based analysis, and histological image preprocessing workflows.
Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.
Computational pathology toolkit for analyzing whole-slide images (WSI) and multiparametric imaging data. Use this skill when working with histopathology slides, H&E stained images, multiplex immunofluorescence (CODEX, Vectra), spatial proteomics, nucleus detection/segmentation, tissue graph construction, or training ML models on pathology data. Supports 160+ slide formats including Aperio SVS, NDPI, DICOM, OME-TIFF for digital pathology workflows.
Lightweight WSI tile extraction and preprocessing. Use for basic slide processing, tissue detection, tile extraction, and stain normalization for H&E images. Best for simple pipelines, dataset preparation, and quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.
Take artokun/civitai from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.