| AI image generation via gpt-image-2, nano-banana, and MiniMax image-01. Use when the user wants to generate or create an image / picture / still.
npx skills add https://github.com/0xsline/OpenChatCut --skill image-gen
Generate AI images via submit_image (configured provider keys only). Prefer one clear still per request unless the user asked for variants.
| Model | Reference | Strengths | Max refs |
| --- | --- | --- | --- |
| gpt-image-2 | references/gpt-image-2.md | Best text rendering, strongest prompt adherence | 16 |
| nano-banana | references/nano-banana.md | Strongest reference-image fidelity | 14 |
| image-01 | references/image-01.md | MiniMax stills / live style; one subject reference via R2 | 1 |
gpt-image-2 when that key is on.nano-banana.image-01.IMPORTANT: Before generating, READ the chosen model's reference.
| 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 | 512px, 1K, 2K, 4K (model-specific) | 1K |
| width / height | GPT Image: 512–3840, /16; MiniMax: 512–2048, /8 | — |
| 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; image-01 max 9) | 1 |
| promptOptimizer | MiniMax image-01 only — prompt_optimizer | false |
| seed | MiniMax image-01 only | — |
| maskAssetId, background, moderation, inputFidelity | GPT Image edit/output controls | — |
| outputFormat, outputCompression | GPT Image PNG/JPEG/WebP controls | PNG |
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",
});
// MiniMax (optional single subject reference; R2 must be configured for refs)
submit_image({
model: "image-01",
prompt: "matte product bottle on marble, soft studio light",
name: "Bottle still",
promptOptimizer: false,
});
OpenChatCut’s submit_image may return completed pool assets synchronously depending on the provider path. If a jobId is returned, use track_progress; otherwise treat the asset ids in the result as done.
name with a short descriptive asset name.Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
Improves the quality of images, especially screenshots, by enhancing resolution, sharpness, and clarity. Perfect for preparing images for presentations, documentation, or social media posts.
Downloads videos from YouTube and other platforms for offline viewing, editing, or archival. Handles various formats and quality options.
Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.
Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.
Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.
This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.
Take 0xsline/image-gen from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.