/imagegen

name: "imagegen"
description: "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."

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/image_gen.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.
• Use `--out` or `--out-dir` to control output paths; keep filenames stable and descriptive.

Dependencies (install if missing)

Prefer `uv` for dependency management.

Python packages:

uv pip install openai pillow

If `uv` is unavailable:

python3 -m pip install openai pillow

Environment

• `OPENAI_API_KEY` must be set for live API calls.

If the key is missing, give the user these steps: 1. Create an API key in the OpenAI platform UI: https://platform.openai.com/api-keys 2. Set `OPENAI_API_KEY` as an environment variable in their system. 3. Offer to guide them through setting the environment variable for their OS/shell if needed.

• Never ask the user to paste the full key in chat. Ask them to set it locally and confirm when ready.

If installation isn't possible in this environment, tell the user which dependency is missing and how to install it locally.

Defaults & rules

• Use `gpt-image-1.5` unless the user explicitly asks for `gpt-image-1-mini` or explicitly prefers a cheaper/faster model.
• Assume the user wants a new image unless they explicitly ask for an edit.
• Require `OPENAI_API_KEY` before any live API call.
• Use the OpenAI Python SDK (`openai` package) for all API calls; do not use raw HTTP.
• If the user requests edits, use `client.images.edit(...)` and include input images (and mask if provided).
• Prefer the bundled CLI (`scripts/image_gen.py`) over writing new one-off scripts.
• Never modify `scripts/image_gen.py`. If something is missing, ask the user before doing anything else.
• If the result isn’t clearly relevant or doesn’t satisfy constraints, iterate with small targeted prompt changes; only ask a question if a missing detail blocks success.

Prompt augmentation

Reformat user prompts into a structured, production-oriented spec. Only make implicit details explicit; do not invent new requirements.

Use-case taxonomy (exact slugs)

Classify each request into one of these buckets and keep the slug consistent across prompts and references.

Generate:

• photorealistic-natural — candid/editorial lifestyle scenes with real texture and natural lighting.
• product-mockup — product/packaging shots, catalog imagery, merch concepts.
• ui-mockup — app/web interface mockups that look shippable.
• infographic-diagram — diagrams/infographics with structured layout and text.
• logo-brand — logo/mark exploration, vector-friendly.
• illustration-story — comics, children’s book art, narrative scenes.
• stylized-concept — style-driven concept art, 3D/stylized renders.
• historical-scene — period-accurate/world-knowledge scenes.

Edit:

• text-localization — translate/replace in-image text, preserve layout.
• identity-preserve — try-on, person-in-scene; lock face/body/pose.
• precise-object-edit — remove/replace a specific element (incl. interior swaps).
• lighting-weather — time-of-day/season/atmosphere changes only.
• background-extraction — transparent background / clean cutout.
• style-transfer — apply reference style while changing subject/scene.
• compositing — multi-image insert/merge with matched lighting/perspective.
• sketch-to-render — drawing/line art to photoreal render.

Quick clarification (augmentation vs invention):

• If the user says “a hero image for a landing page”, you may add *layout/composition constraints* that are implied by that use (e.g., “generous negative space on the right for headline text”).
• Do not introduce new creative elements the user didn’t ask for (e.g., adding a mascot, changing the subject, inventing brand names/logos).

Template (include only relevant lines):

Use case: <ta