From chatcut
Generates AI images via gpt-image-2 and nano-banana models with configurable aspect ratio, size, quality, and reference-image support.
How this skill is triggered — by the user, by Claude, or both
Slash command
/chatcut:image-genThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Generate AI images through the backend generation API. Submit-only: creates a generation job and returns a `jobId`.
Generate AI images through the backend generation API. Submit-only: creates a generation job and returns a jobId.
After submission, use track_progress tool to check status or wait for completion.
| Model | Strengths | Max refs |
|---|---|---|
gpt-image-2 | Best text rendering, strongest prompt adherence | 10 |
nano-banana | Strongest reference-image fidelity | 14 |
gpt-image-2 is the default.nano-banana is the reference-heavy choice. Use it when reference-image fidelity matters more than text rendering, or when the user needs more than 10 reference images.IMPORTANT: Before generating, READ the model's reference document for params, limits, and prompt tips:
gpt-image-2 → references/gpt-image-2.mdnano-banana → references/nano-banana.md| Param | Values | Default |
|---|---|---|
aspectRatio | 1:1, 16:9, 9:16, 4:3, 3:4, 3:2, 2:3, 4:5, 5:4, 21:9 | 16:9 |
imageSize | 1K, 2K, 4K | 1K |
quality | low, medium, high, auto (gpt-image-2 only) | high |
referenceAssetIds | Array of project asset ids — backend resolves bytes server-side | — |
name | Short descriptive asset name shown in the library | — |
count | Number of images to generate (1–10, each becomes a separate job) | 1 |
imageSize: "2K" or "4K" when the user explicitly asks. Warn that 2K/4K are EXPERIMENTAL and may be slower.Use when the user provides source material to edit, blend, or use as visual guidance (e.g. "change the background", "combine these into a poster").
referenceAssetIds. The backend fetches and encodes them server-side — never pull the asset bytes yourself.referenceAssetIds.// Basic generation
submit_image({
model: "gpt-image-2",
prompt: "a cute orange cat",
name: "Cat",
});
// With quality (gpt-image-2 only)
submit_image({
model: "gpt-image-2",
prompt: "hero poster with bold title",
quality: "high",
name: "Hero Poster",
});
// With reference images — pass project asset ids; backend resolves bytes
submit_image({
model: "gpt-image-2",
prompt: "change background to beach",
referenceAssetIds: ["<assetId>"],
name: "Beach Edit",
});
// Reference-heavy with nano-banana
submit_image({
model: "nano-banana",
prompt: "composite poster",
referenceAssetIds: ["<id1>", "<id2>"],
name: "Composite",
});
// Multiple images
submit_image({
model: "gpt-image-2",
prompt: "product shots",
count: 3,
name: "Product",
});
After submission, call the track_progress tool: action=status jobIds=<jobId> to poll, action=wait jobIds=<jobId> to block until terminal.
name with a short descriptive asset name.track_progress tool for that.npx claudepluginhub chatcut-inc/agent-plugin --plugin chatcutGenerates AI images with nano-banana CLI using Gemini Flash/Pro. Supports 512-4K sizes, aspect ratios, style references, green screen transparency, exact dimensions, cost tracking.
Image generation via Gemini 3 Pro Image: aspect ratio, resolution, reference images. Use when creating artwork, product photos, or mockups with AI.
Generates and edits images using Google Gemini Pro and Flash models via Python CLI scripts. Handles prompts, image references, aspect ratios, and outputs to Downloads. Activates on image requests.