Agent skill · design visual · davila7
imagegen
Use when the user asks to generate or edit images via the OpenAI Image API (for example: generate image, edit/inpaint/mask, background removal or replacement, transparent background, product shots, concept art, covers, or batch variants); run the bundled CLI (`scripts/image_gen.py`) and require `OPENAI_API_KEY` for live calls.
Why this skill is useful
Adds specific commands and workflows for generating and editing images using the OpenAI Image API that the AI wouldn't reliably generate on its own.
What it needs
Requires openai, pillow, uv installed locally. Requires openai account access. About 5k tokens when loaded. Last updated 2026-08-06. 30,138 stars on the source repository.
What this skill does
Image Generation Skill Generates or edits images for the current project (e.g., website assets, game assets, UI mockups, product mockups, wireframes, logo design, photorealistic images, infographics). Defaults to gpt-image-1.5 and the OpenAI Image API, and prefers the bundled CLI for deterministic, reproducible runs. When to use Generate a new image (concept art, product shot, cover, website hero) Edit an existing image (inpainting, masked edits, lighting or weather transformations, background replacement, object removal, compositing, transparent background) Batch runs (many prompts, or many variants across prompts) Decision tree (generate vs edit vs batch) If the user provides an input image (or says “edit/retouch/inpaint/mask/translate/localize/change only X”) → edit Else if the user needs many different prompts/assets → generate-batch Else → generate Workflow 1. Decide intent: generate vs edit vs batch (see decision tree above). 2. Collect inputs up front: prompt(s), exact text (verbatim), constraints/avoid list, and any input image(s)/mask(s). For multi-image edits, label each input by index and role; for edits, list invariants explicitly. 3. If batch: write a temporary JSONL under tmp/ (one job per line), run once, then delete the JSONL. 4. Augment prompt into a short labeled spec (structure + constraints) without inventing new creative requirements. 5. Run the bundled CLI (scripts/imagegen.py) with sensible defaults (see references/cli.md). 6. For complex edits/generations, inspect outputs (open/view images) and validate: subject, style, composition, text accuracy, and invariants/avoid items. 7. Iterate: make a single targeted change (prompt or mask), re-run, re-check. 8. Save/return final outputs and note the final prompt + flags used. Temp and output conventions Use tmp/imagegen/ for intermediate files (for example JSONL batches); delete when done. Write final artifacts under output/imagegen/ when working in this repo. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills davila7/imagegen