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 timoncool/civitai-mcp-ultimate --skill civitai-mcp-ultimategit clone --depth 1 https://github.com/timoncool/civitai-mcp-ultimateWrote 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/timoncool/civitai-mcp-ultimate/civitai-mcp-ultimate)<a href="https://agentmods.dev/skills/timoncool/civitai-mcp-ultimate/civitai-mcp-ultimate"><img src="https://agentmods.dev/badge/skills/timoncool/civitai-mcp-ultimate/civitai-mcp-ultimate/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/timoncool/civitai-mcp-ultimate/civitai-mcp-ultimate"><img src="https://agentmods.dev/badge/skills/timoncool/civitai-mcp-ultimate/civitai-mcp-ultimate.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00065 | $0.02683 |
| Opus 5 | $0.00032 | $0.01341 |
| Sonnet 5 | $0.00013 | $0.00537 |
| Haiku 4.5 | $0.00006 | $0.00268 |
Grade A, and why
civitai-mcp-ultimate 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 12d 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.
-> ready-to-paste curl/wget/PowerShell How it starts
The opening of the file, as written. The whole thing — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Civitai MCP Ultimate — 14 Tools Reference
Quick Tool Map
| Goal | Tool | Key Params |
|---|---|---|
| Find models by name | search_models |
query (Meilisearch), types, base_model |
| Find models by creator | search_models |
username (most reliable) |
| Batch fetch by IDs | search_models |
ids=[123, 456] |
| Full model details | get_model |
model_id |
| Version details + hashes | get_model_version |
version_id |
| Identify model from file | get_model_version_by_hash |
SHA256/AutoV2/CRC32/BLAKE3 |
| Top checkpoints | get_top_checkpoints |
base_model, period |
| Top LoRAs | get_top_loras |
base_model, nsfw |
| Browse images/videos | browse_images |
model_id, content_type, tag, base_model |
| Trending images/videos | get_top_images |
sort, period, content_type, browsing_level |
| Example images for model | get_model_images |
model_id |
| Extract best prompts | get_image_generation_data |
model_id |
| Download URL | get_download_url |
version_id |
| Download commands | get_download_info |
model_id, comfyui_path |
Common Recipes (Quick Reference)
Top image by likes yesterday, specific base model
get_top_images(sort="Most Reactions", period="Day", content_type="image", base_model="Flux.1 D", limit=1)
Top video by comments this week
get_top_images(sort="Most Comments", period="Week", content_type="video", limit=1)
Most popular SDXL model this month
search_models(base_model="SDXL 1.0", sort="Most Downloaded", period="Month", limit=1)
Top 5 images from the most popular Flux model today
search_models(types=["Checkpoint"], base_model="Flux.1 D", sort="Most Downloaded", period="Day", limit=1)
-> get model_id
get_model_images(model_id=ID, limit=5)
Community images for a model (sorted by reactions)
browse_images(model_id=ID, sort="Most Reactions", period="Month", limit=5)
NOTE: model_id auto-resolves to the latest version. To see images from a specific version, use model_version_id directly:
get_model(model_id=ID) # -> see all versions with IDs
browse_images(model_version_id=VERSION_ID, sort="Most Reactions", period="AllTime", limit=5)
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.
- 12d ago First seen · 216 lines · 65 tokens per session scan A 2db9470f2ec0
civitai-mcp-ultimate is a skill published in the GitHub repository timoncool/civitai-mcp-ultimate (20 stars, last pushed 5d ago), licensed MIT. It adds 65 tokens to every session and 2,683 once invoked, about $0.0003 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 2026-08-30.
Other skills, from other repositories
civitai-depot
Discover Civitai models and pin weights into the comfyops models depot.
Prompt craft for ComfyUI generation
Use when writing prompts for ComfyUI image or video models: choosing tags vs literary format, fixing composition and anatomy artifacts, picking samplers per LoRA, and writing short movement prompts for video. Neutral examples only.
diffusers-ascend-pipeline
A guide for running image and video generation pipelines on Huawei Ascend NPUs with the Diffusers library. Diffusers is a software library for using generative models, and the guide covers model pipelines, memory settings, LoRA adapters, and multi-card inference.
implementing-llms-litgpt
Implements and trains LLMs using Lightning AI's LitGPT with 20+ pretrained architectures (Llama, Gemma, Phi, Qwen, Mistral). Use when need clean model implementations, educational understanding of architectures, or production fine-tuning with LoRA/QLoRA. Single-file implementations, no abstraction layers.
llama-factory
Expert guidance for fine-tuning LLMs with LLaMA-Factory - WebUI no-code, 100+ models, 2/3/4/5/6/8-bit QLoRA, multimodal support.
tinker-fine-tuning
Provides guidance for fine-tuning LLMs using the Tinker cloud training API from Thinking Machines Lab. Use when running supervised fine-tuning, reinforcement learning (GRPO/PPO), or LoRA training on cloud GPUs via Tinker's managed infrastructure instead of local compute.