Generate and edit images using OpenAI's GPT Image 1.5 model. Use when the user asks to generate, create, edit, modify, change, alter, or update images. Also use when user references an existing image file and asks to modify it in any way (e.g., "modify this image", "change the background", "replace X with Y"). Supports text-to-image generation and image editing with optional mask. DO NOT read the image file first - use this skill directly with the --input-image parameter.
npx skills add https://github.com/intellectronica/agent-skills --skill gpt-image-1-5
Generate new images or edit existing ones using OpenAI's GPT Image 1.5 model.
Run the script using absolute path (do NOT cd to skill directory first):
Generate new image:
uv run ~/.claude/skills/gpt-image-1-5/scripts/generate_image.py --prompt "your image description" --filename "output-name.png" [--quality low|medium|high] [--size 1024x1024|1024x1536|1536x1024|auto] [--background transparent|opaque|auto] [--api-key KEY]
Edit existing image (without mask - full image edit):
uv run ~/.claude/skills/gpt-image-1-5/scripts/generate_image.py --prompt "editing instructions" --filename "output-name.png" --input-image "path/to/input.png" [--size 1024x1024|1024x1536|1536x1024|auto] [--api-key KEY]
Edit existing image (with mask - precise inpainting):
uv run ~/.claude/skills/gpt-image-1-5/scripts/generate_image.py --prompt "what to put in masked area" --filename "output-name.png" --input-image "path/to/input.png" --mask "path/to/mask.png" [--size 1024x1024|1024x1536|1536x1024|auto] [--api-key KEY]
Important: Always run from the user's current working directory so images are saved where the user is working, not in the skill directory.
Map user requests:
mediumlowhighMap user requests:
1024x10241024x10241024x15361536x1024The script checks for API key in this order:
--api-key argument (use if user provided key in chat)OPENAI_API_KEY environment variableIf neither is available, the script exits with an error message.
Generate filenames with the pattern: yyyy-mm-dd-hh-mm-ss-name.png
Format: {timestamp}-{descriptive-name}.png
yyyy-mm-dd-hh-mm-ss (24-hour format)x9k2, a7b3)Examples:
2025-12-17-14-23-05-japanese-garden.png2025-12-17-15-30-12-sunset-mountains.png2025-12-17-16-45-33-robot.png2025-12-17-17-12-48-x9k2.pngBoth editing modes use the Image API (images.edit endpoint) with gpt-image-1.5 for reliable results.
When the user wants to modify an existing image without specifying exact regions:
--input-image parameter with the path to the imageWhen the user wants to edit specific regions:
--input-image parameter with the path to the image--mask parameter with a PNG mask fileCommon editing tasks: add/remove elements, change style, adjust colors, replace backgrounds, etc.
For generation: Pass user's image description as-is to --prompt. Only rework if clearly insufficient.
For editing: Pass editing instructions in --prompt (e.g., "add a rainbow in the sky", "make it look like a watercolor painting")
Preserve user's creative intent in both cases.
Generate new image:
uv run ~/.claude/skills/gpt-image-1-5/scripts/generate_image.py --prompt "A serene Japanese garden with cherry blossoms" --filename "2025-12-17-14-23-05-japanese-garden.png" --quality high --size 1536x1024
Generate with transparent background:
uv run ~/.claude/skills/gpt-image-1-5/scripts/generate_image.py --prompt "A cute cartoon cat mascot" --filename "2025-12-17-14-25-30-cat-mascot.png" --background transparent --quality high
Edit existing image (full image):
uv run ~/.claude/skills/gpt-image-1-5/scripts/generate_image.py --prompt "make the sky more dramatic with storm clouds" --filename "2025-12-17-14-27-00-dramatic-sky.png" --input-image "original-photo.jpg"
Edit with mask (inpainting):
uv run ~/.claude/skills/gpt-image-1-5/scripts/generate_image.py --prompt "a flamingo swimming" --filename "2025-12-17-14-30-00-lounge-flamingo.png" --input-image "lounge.png" --mask "mask.png"
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 intellectronica/gpt-image-1-5 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.