Image Agent Skill

Images with Guaardvark

Read setup first if the backend or the comfyui plugin state is unknown.

One image: MCP generate_image

• prompt is scene, pose, lighting, setting. Plain prose. Do not paste JSON or tag soup; the

default model (Z-Image Turbo) reads prompts as language, and SD-era tag lists hurt it.

• model default auto picks the best downloaded model. Only override when the user names one:

zimage-turbo, krea2-turbo, krea2-raw, flux-dev, sd-xl, sdxl-turbo,

realistic-vision, epic-realism.

• width / height: 512, 768 or 1024. style: realistic, artistic, anime, photographic, digital-art.
• Consistent character: pass subject_ids=[<cast id>] as its own array. Never put the

trigger word alone in the prompt and expect the LoRA to load. Find ids with

GET /api/cast-library (see the cast skill).

• On-image text: quote the exact words in double quotes inside the prompt.
• The tool returns the image URL (/api/outputs/generated_images/<file>.png, relative to the

backend), the model that ran, steps, seed and whether a Cast LoRA was applied. Show the URL

and the prompt you used. Measured: 768x768 on Z-Image Turbo in ~20 s on a free 16 GB card.

• Over MCP the call queues by default (wait_for_result defaults to false there) and

returns Image queued as batch ImageBatch_... at once. Poll

get_generation_status(batch_id=...) every few seconds until completed; it returns the

file URL. Pass wait_for_result: true to block for the render instead (allowed up to 30

minutes). A call that exceeds the server's timeout answers with an error that says the

render is still running; it is not lost.

• A failed call carries the backend's reason (plugin off, out of memory, bad model). Read it

and act on it; inspect_gpu and GET /api/plugins/status are the two checks that resolve most.

Edit an existing image: MCP edit_image

• instruction is the change ("put a cowboy hat on him", "make the shirt red"). The image the

user just attached is used automatically; otherwise pass image as a path or URL.

• model auto uses FLUX.1 Kontext when installed, else img2img on the current model.
• For a brand-new picture use generate_image, not this.

Many images: REST batch

B=${GUAARDVARK_URL:-http://localhost:5000}
curl -s -X POST $B/api/batch-image/generate/prompts -H 'Content-Type: application/json' -d '{
  "prompts": ["prompt one", "prompt two"],
  "model": "auto",
  "subject_ids": []
}'

• prompts may be strings or {"prompt": "..."} objects. There is a per-batch maximum; if the

server answers 400 "Too many prompts", split the list.

• Optional adapters (user LoRAs from the models skill) and subject_ids (Cast Library).
• The response is data.batch_id (ImageBatch_<date>_<n>). Poll

GET $B/api/batch-image/status/<batch_id>?include_results=true: status goes

running → completed, with completed_images / total_images, output_dir, and one

results[] entry per prompt (success, image_path, thumbnail_path, generation_time,

metadata.model_used). A contact sheet: GET $B/api/batch-image/preview/<batch_id>; one file:

GET $B/api/batch-image/image/<batch_id>/<image_name> (the basename of image_path).

Cancel with POST $B/api/batch-image/cancel/<batch_id>. Measured: one 1024x1024 prompt

completed in ~30 s.

• Helpers: POST /api/batch-image/enhance-prompt, /analyze-prompt, /expand-concept (JSON

body with the prompt) when the user wants prompt help before spending GPU time.

Rules

• Say which model actually ran (the response names it). Do not promise a model that is not installed.
• Generation time depends on the GPU; a first image after Ollama held the card can take longer

because the orchestrator swaps models. That is normal.

• Never upload the user's images anywhere. Everything here is local.