Imagegen

imagegen

Generate and edit images for the current project exclusively through the Codex CLI (codex exec) and its built-in image_gen tool, counting toward the user's Codex usage limits. This skill targets ChatGPT Images 2.5; the backend served to a session depends on availability.

Requirements

• Codex CLI installed (codex --version), on a recent version whose agent exposes the built-in image_gen tool.
• Logged in with a ChatGPT account: codex login.
• For local alpha inspection: Python 3 with Pillow (python3 -m pip install pillow).

When to use

• Generate a new image: concept art, product shot, hero/banner, game asset, infographic.
• Generate using one or more reference images for style, composition, or mood.
• Edit an existing image: object removal/replacement, lighting or weather changes, background replacement, compositing, text localization, sketch-to-render.
• Produce a transparent-background cutout (see references/transparency.md).
• Produce several variants of one asset.

When not to use

• Extending or matching an existing SVG/vector icon set, logo system, or illustration library in the repo — edit those natively.
• Simple shapes, diagrams, wireframes, or icons better produced in SVG, HTML/CSS, or canvas.
• Any task where the user wants deterministic code-native output rather than a generated bitmap.

Images 2.5 model notes

Use the built-in Codex tool schema. Report an exact backend version only when runtime metadata confirms it.

• Strong instruction following, layout control, and in-image text rendering — quote exact text verbatim in the prompt.
• Produce transparency natively through image_gen only; validate it using references/transparency.md.
• Use reference images to preserve subject identity and state exactly which details each edit must retain.
• Express aspect ratio, resolution intent, and polish level in natural language (for example "wide 16:9 landscape hero"). Set additional tool arguments only when the exposed schema supports them; verify the output rather than treating requested dimensions as guaranteed.

Workflow

1. Decide the intent: generate (new image, or references used only for style/mood) vs edit (parts of an input image must be preserved). Assume generate unless the user clearly wants to change an existing image.
2. Collect inputs up front: prompt(s), exact text to render (verbatim), constraints/avoid list, input images with an explicit role each (edit target, style reference, compositing insert).
3. Shape the image prompt using references/prompting.md: preserve specific requests and add only success-relevant details.
4. If transparency is needed, apply the native-transparency prompt from references/transparency.md.
5. Run Codex non-interactively (below), instructing it to use its built-in image_gen tool and to copy the final image to an explicit workspace path.
6. Verify the output file exists, then inspect it with an available image-viewing tool: subject, style, composition, text accuracy, constraints respected.
7. For transparency, complete the alpha and visual checks in references/transparency.md before accepting the output.
8. Iterate with a single targeted change per round; for edits, repeat invariants (change only X; keep Y unchanged) every iteration.
9. Save non-destructively: never overwrite an existing project asset unless the user asked for replacement — use a versioned sibling name (hero-v2.png). For batches, identify selected finals without deleting other outputs unless the user authorizes cleanup.
10. Report the final saved path(s) and the final image prompt used.

Driving Codex

Use codex exec --sandbox workspace-write to allow saving the result in the workspace. Defaults can depend on configuration, so select the sandbox explicitly. Name an output path inside the workspace and use the actual file path returned by the tool when copying; do not assume a fixed generation directory.

Generate:

codex exec --sandbox workspace-write 'Using your built-in image_gen tool, generate this image:

Create a wide 16:9 product photograph of a ceramic coffee mug for a landing-page hero. Use soft studio light from the left and a minimal background, with no text or logos.

Then copy the final image to output/imagegen/mug-hero.png in this directory and reply with that path.'

Edit — attach the input image with -i/--image (repeat the flag for multiple inputs and reference them by index). Put -- before the prompt so the image option cannot consume it as another file:

codex exec --sandbox workspace-write -i product.png -- 'Using your built-in image_gen tool, edit the attached image (Image 1, the edit target):

Replace only the background with a warm sunset gradient. Keep the product and its edges unchanged; add no text or logos.

Then copy the final image to output/imagegen/product-sunset.png in this directory and reply with that path.'

Variants: run one codex exec call per variant with a distinct output filename (logo-v1.png, logo-v2.png, …). Serialize the calls rather than parallelizing.

Skill execution rules (not platform limits):

• One image per codex exec call; keep the instruction limited to generation + copy, no other repo changes.
• Use PNG or JPEG inputs, documented for -i; verify support in the installed version before relying on other formats.
• If codex is missing, unauthenticated, or reports image_gen unavailable, stop and tell the user (install: https://developers.openai.com/codex/cli, then codex login) — the only generation path is the user's Codex subscription, never an API key or one-off SDK runner.