Optimizes image generation prompts using Subject-Context-Style structure. Use this skill when generating images, creating illustrations, photos, visual assets, editing images, or crafting prompts for any image generation model.
npx skills add https://github.com/shinpr/mcp-image --skill image-generation
Enhance every image generation prompt around three core elements:
Return the enhanced prompt as a single flowing paragraph. When the user provides multiple requests, return each as a separate enhanced prompt under a labeled heading.
Add concrete visual details for any Subject/Context/Style element not specified by the user:
When a photographic look is appropriate:
Convey mood through environmental details:
When the image should contain readable text (signs, labels, titles, typography):
"OPEN 24 HOURS" in bold sans-serifWhen the same character must be recognizable across multiple images:
When combining multiple visual elements in one scene:
When depicting real places, cultures, or historical elements:
Tailor the prompt to the intended use:
| Purpose | Emphasis |
|---------|----------|
| Product photo | Clean background, studio lighting, commercial appeal |
| UI mockup | Flat design elements, consistent spacing, screen-appropriate |
| Presentation slide | Bold composition, clear focal point, text-friendly layout |
| Social media | Eye-catching, vibrant, crop-friendly aspect ratio |
| Book/album cover | Typography space, dramatic mood, symbolic elements |
When modifying an existing image:
This skill covers static image prompt enhancement only. It does not cover video generation, 3D rendering, or image analysis/description.
Input: "A happy dog in a park"
Enhanced: "Golden retriever mid-leap catching a red frisbee, ears flying, tongue out in joy, in a sunlit urban park. Soft morning light filtering through oak trees creates dappled shadows on emerald grass. Background shows families on picnic blankets, slightly out of focus. Shot from low angle emphasizing the dog's athletic movement, with motion blur on the paws suggesting speed."
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 shinpr/image-generation 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.