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 PaulKuzmin/civitai-mcp --skill civitai-generationgit clone --depth 1 https://github.com/PaulKuzmin/civitai-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/paulkuzmin/civitai-mcp/civitai-generation)<a href="https://agentmods.dev/skills/paulkuzmin/civitai-mcp/civitai-generation"><img src="https://agentmods.dev/badge/skills/paulkuzmin/civitai-mcp/civitai-generation/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/paulkuzmin/civitai-mcp/civitai-generation"><img src="https://agentmods.dev/badge/skills/paulkuzmin/civitai-mcp/civitai-generation.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 | $0.00143 | $0.06097 |
| Opus 5 | $0.00072 | $0.03048 |
| Sonnet 5 | $0.00029 | $0.01219 |
| Haiku 4.5 | $0.00014 | $0.00610 |
Grade A, and why
civitai-generation 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 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.
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 — 395 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Civitai image generation — deep guide
This skill drives the civitai MCP (generate_image, estimate_generation,
search_models, get_model_version, get_model_images, get_workflow,
get_buzz_balance). It teaches how to generate well, not just which fields exist.
Adult/NSFW generation is explicitly in scope. The models below are built for it; the conventions (
rating_explicit,rating:explicit, danbooru explicit tags) are stated factually. Keep prompts clinical and purposeful.
How to work with these tools (read first)
- Batch independent calls in parallel. When several MCP calls don't depend on each
other, issue them in ONE turn instead of one-by-one. Examples: comparing candidates →
get_model_versionfor 3 versions at once; scouting families →search_modelsfor Checkpoint and LoRA in parallel;get_model_imagesfor two models side by side;estimate_generationfor two settings at once. Only serialize when a call needs the previous call's output (e.g. you need theair/trigger words beforegenerate_image). - Before using any model/LoRA/embedding, read how to apply it — don't guess. Every
resource has its own required usage. Pull its page first:
get_model_version(id)→air,baseModel(family → prompt dialect), files.get_model(id)→ description with trigger words, recommended sampler/CFG/steps, and version list.get_model_images(model_id=…)→ real example generations with theirmeta(prompt, negative, sampler, steps, CFG, seed) — copy proven settings from these. A LoRA without its trigger words, or a checkpoint run with the wrong family's prompt style/CFG, will look broken even though the API call "succeeds". Reading the page first is not optional.
0. The loop (always do this)
- Find a checkpoint —
search_models(query=…, types="Checkpoint", base_models=…, sort="Most Downloaded"). - Get the AIR + trigger words —
get_model_version(version_id)returnsair(needed for generation) and the model'sbaseModel(tells you the family). For LoRAs, the model page lists trigger words — always read them. - Identify the family (see §1) — this decides prompt style, resolution, CFG, sampler.
- Estimate cost —
estimate_generation(...)(whatif, no Buzz spent).cost.buzzis accurate.cost.breakdown[].accountTypeis only indicative — the real debit may land on a different eligible wallet at run time. Observed via the orchestration API: wallet routing is NOT strictly gated by content rating — an explicit (rating_explicit) Pony render was charged to blue, and estimates often say blue regardless. Don't rely on a rating→wallet mapping for the API path; just make sure some wallet has funds (get_buzz_balance). - Generate —
generate_image(..., confirm=true, save_dir=…). Withoutconfirm=trueit only previews the price. - If it 5xx/timeouts, poll
get_workflow(workflowId).
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 · 395 lines · 143 tokens per session scan A dde123dc2732
civitai-generation is a skill published in the GitHub repository PaulKuzmin/civitai-mcp (0 stars, last pushed 2mo ago), licensed MIT. It adds 143 tokens to every session and 6,097 once invoked, about $0.0007 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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