civitai-gen

name	civitai-gen
description	Generate images, videos, audio, and more using Civitai's orchestration API. Use when the user wants text-to-image, video generation (11+ engines), text-to-speech, music, transcription, bulk batches, experiment sweeps, or buzz cost estimation. Not for browsing or searching Civitai models (use the Civitai MCP server).
license	MIT
compatibility	Requires Node.js 18+ (native fetch) and a CIVITAI_API_KEY. Network access required. ffmpeg optional (audio post-processing only).
metadata	{"author":"Civitai","version":"1.0.5","homepage":"https://github.com/civitai/civitai-gen-skill"}

civitai-gen

Unified CLI for Civitai's orchestration Workflow API. All generation types share the same workflow lifecycle: submit, poll, download.

Setup

Requires Node.js 18+. Set CIVITAI_API_KEY either as an environment variable or in a .env file in this skill's directory (next to generate.mjs). Copy .env.example to .env to start. Get a key at: https://civitai.com/user/account

Paths below are relative to this skill's directory. Run the scripts from there (cd into it), or prefix with the install path your runtime uses.

Capabilities

Command	What it does	Details
wait	Submit + poll + download (default)	All-in-one blocking generation
submit	Fire-and-forget, returns workflow ID	For async workflows
status	Check workflow progress	--poll for live updates
download	Fetch completed media	From a workflow ID
cost	Dry-run buzz estimation	whatif=true, 0 buzz spent
engines	List video engines + live status	11+ engines
tts	Text-to-speech	See docs/tts.md
music	Music/song generation (ACE Step 1.5)	See docs/music.md
transcribe	Speech-to-text transcription	See docs/transcription.md

Quick Examples

# Image (defaults to Flux.1, 4 images)
node generate.mjs wait --prompt "A knight at sunset" -o ./out

# Multiple concurrent prompts
node generate.mjs wait --prompt "A warrior" --prompt "A mage" -o ./out

# Video (VEO 3)
node generate.mjs wait --engine veo3 --prompt "A robot walking" -o ./out

# Text-to-speech
node generate.mjs tts --text "Hello world" --speaker serena -o ./out

# Music
node generate.mjs music --prompt "upbeat electronic dance track" -o ./out

# Transcription
node generate.mjs transcribe --media-url "https://example.com/audio.mp3" -o ./out

# Cost check (any type, 0 buzz)
node generate.mjs cost --prompt "A cat" -n 100
node generate.mjs cost --engine veo3 --prompt "A robot" --duration 8

# Experiment mode (wildcard expansion)
node experiment.mjs --spec experiment.json -o ./out

Posting What You Generate

Generate an image (the skill saves it locally), then post the local file in one command:

node generate.mjs wait --prompt "a red apple" -n 1 -o ./out
node mcp-cli.mjs post-image ./out/step_0-0.png --title "My apple"
# -> Post published. URL: https://civitai.com/posts/<id>

post-image reads the local file, uploads it, and publishes the post — it prints the public URL. Pull mcp-cli.mjs first if you don't have it (curl -fsSL https://mcp.civitai.com/cli -o mcp-cli.mjs); CIVITAI_API_KEY is required.

Flags: --title (defaults to the filename), --detail, --nsfw <level>, --draft (leave unpublished), --json. For video/audio, or to attach a pre-uploaded image by UUID, call create_post directly (node mcp-cli.mjs schema create_post) — and prefer posting the local file over the generate output's remoteUrls[].url, which is a short-lived signed URL that expires quickly.

Choosing an Engine & Model

Read docs/engines.md to pick the right generator. The key split:

Path	Engines	How to pick the model
Open-weight ecosystem	SD1, SDXL, Pony, Illustrious, Flux.1/2, Qwen, Z-Image, Chroma, Anima	Find a checkpoint AIR + compatible LoRAs via the Civitai MCP (search_models / get_model_version) → --model / --resources
Closed API engine	OpenAI, Google/Gemini, Seedream, Grok, MAI, ERNIE + all video/audio	Engine name only — no checkpoint search, no LoRA

Model discovery uses the Civitai MCP server (hosted at https://mcp.civitai.com/mcp) — call its tools to search checkpoints/LoRAs and get AIR URNs. Don't search inside this skill. A LoRA's base model must match the checkpoint's ecosystem.

If the Civitai MCP isn't connected, add it (browse tools need no API key): claude mcp add --transport http civitai https://mcp.civitai.com/mcp

Can't add MCP config in your runtime? Pull the zero-dep CLI (Node 18+) and call tools over the shell instead: curl -fsSL https://mcp.civitai.com/cli -o mcp-cli.mjs node mcp-cli.mjs call search_models '{"query":"anime portrait","type":"Checkpoint","supportsGeneration":true,"baseModel":"SDXL 1.0"}'

# Call these Civitai MCP tools directly (they return AIR URNs ready for --model / --resources):
search_models     { query: "anime portrait", type: "Checkpoint", supportsGeneration: true, baseModel: "SDXL 1.0" }
search_models     { query: "neon style", type: "LORA", baseModel: "SDXL 1.0" }   # LoRA base MUST match checkpoint
get_model_version { ids: [<versionId>] }   # full version details + AIR URN + trigger words

Engine availability/params drift — run node generate.mjs engines for the live list and see https://developer.civitai.com/orchestration/recipes for per-engine parameters.

Domain-Specific Docs

For detailed parameters, read the relevant doc:

• Images: --prompt, --model, --resources, --aspect, --resolution, --source-image (img2img). Run node generate.mjs --help for full flag list.
• Videos: --engine, --duration, --video-aspect, --generate-audio. Run node generate.mjs engines for available engines.
• TTS: Read docs/tts.md — built-in speakers, voice cloning, style instructions.
• Music: Read docs/music.md — ACE Step 1.5, lyrics, duration.
• Transcription: Read docs/transcription.md — ASR, timestamps, language hints.
• Experiments: Read experiment.mjs --help — wildcards, parameter sweeps, naming.

Workflow Lifecycle

All generation types follow the same pattern:

1. Build steps — each job becomes a workflow step with a $type (textToImage, videoGen, textToSpeech, aceStepAudio, transcription)
2. Submit — POST to orchestration API, get workflow ID
3. Poll — check status until terminal state (succeeded/failed/expired)
4. Download — fetch output media (images, videos, audio files)
5. JSON summary — clean JSON to stdout with paths, costs, and metadata

Use cost (whatif) to estimate buzz before spending. Use --quiet for agent-friendly output.

Guidelines

• Default to 4 images per prompt. Use -n 1 only when a single image is needed.
• For video: always check cost first. Video costs 500-2000+ buzz per clip.
• Multiple --prompt flags create concurrent steps in one workflow.
• Use --bulk file.json for large batches.
• Use experiment mode for systematic parameter sweeps.

Developer Guide

See CLAUDE.md for architecture, module layout, and how to add new step types.