imagegen skill

Image Generation Skill

Generates or edits images for the current project (for example website assets, game assets, UI mockups, product mockups, wireframes, logo design, photorealistic images, or infographics).

Top-level modes and rules

This skill has exactly two top-level modes:

• Default built-in tool mode (preferred): built-in imagegen tool for image generation, editing, and transparent-image requests. Does not require OPENAIAPI_KEY.
• Fallback CLI mode: scripts/imagegen.py CLI. Use when the user explicitly asks for or confirms the CLI/API/model path. Requires OPENAIAPI_KEY.

Within CLI fallback, the CLI exposes three subcommands:

• generate
• edit
• generate-batch

Rules:

• Use the built-in image_gen tool by default for normal image generation and editing requests.
• Do not switch to CLI fallback for ordinary quality, size, or file-path control.
• For transparent images, ask built-in image_gen for a transparent background and preserve the generated alpha.
• Never silently switch from built-in image_gen or CLI gpt-image-2 to CLI gpt-image-1.5; ask the user first unless they explicitly requested gpt-image-1.5.
• The word batch by itself does not mean CLI fallback. If the user asks for many assets or says to batch-generate assets without explicitly asking for CLI/API/model controls, stay on the built-in path and issue one built-in call per requested asset or variant.
• If the built-in tool fails or is unavailable, tell the user the CLI fallback exists and that it requires OPENAIAPIKEY. Proceed only if the user explicitly asks for that fallback.
• If the user explicitly asks for CLI mode, use the bundled scripts/image_gen.py workflow. Do not create one-off SDK runners.
• Never modify scripts/image_gen.py. If something is missing, ask the user before doing anything else.

Built-in save-path policy:

• In built-in tool mode, Codex saves generated images under $CODEXHOME/ by default.
• Do not describe or rely on OS temp as the default built-in destination.
• Do not describe or rely on a destination-path argument (if any) on the built-in imagegen tool. If a specific location is needed, generate first and then move or copy the selected output from $CODEXHOME/generated_images/....
• Save-path precedence in built-in mode:

1. If the user names a destination, move or copy the selected output there.
2. If the image is meant for the current project, move or copy the final selected image into the workspace before finishing.
3. If the image is only for preview or brainstorming, render it inline; the underlying file can remain at the default $CODEXHOME/ path.

• Never leave a project-referenced asset only at the default $CODEXHOME/ path.
• Do not overwrite an existing asset unless the user explicitly asked for replacement; otherwise create a sibling versioned filename such as hero-v2.png or item-icon-edited.png.

Shared prompt guidance for both modes lives in references/prompting.md and references/sample-prompts.md.

Fallback-only docs/resources for CLI mode:

• references/cli.md
• references/image-api.md
• references/codex-network.md
• scripts/image_gen.py

When to use

• Generate a new image (concept art, product shot, cover, website hero)
• Generate a new image using one or more reference images for style, composition, or mood
• Edit an existing image (inpainting, lighting or weather transformations, background replacement, object removal, compositing, transparent background)
• Produce many assets or variants for one task

When not to use

• Extending or matching an existing SVG/vector icon set, logo system, or illustration library inside the repo
• Creating simple shapes, diagrams, wireframes, or icons that are better produced directly in SVG, HTML/CSS, or canvas
• Making a small project-local asset edit when the source file already exists in an editable native format
• Any task where the user clearly wants deterministic code-native output instead of a generated bitmap

Decision tree

Think about two separate questions:

1. Intent: is this a new image or an edit of an existing image?
2. Execution strategy: is this one asset or many assets/variants?

Intent:

• If the user wants to modify an existing image while preserving parts of it, treat the request as edit.
• If the user provides images only as references for style, composition, mood, or subject guidance, treat the request as generate.
• If the user provides no images, treat the request as generate.

Built-in edit semantics:

• Built-in edit mode is for images already visible in the conversation context, such as attached images or images generated earlier in the thread.
• If the user wants to edit a local image file with the built-in tool, first load it with built-in view_image tool so the image is visible in the conversation context, then proceed with the built-in edit flow.
• Do not promise arbitrary filesystem-path editing through the built-in tool.
• If a local file still needs direct file-path control, masks, or other explicit CLI-only parameters, use the explicit CLI fallback only when the user asks for it.
• For edits, preserve invariants aggressively and save non-destructively by default.

Execution strategy:

• In the built-in default path, produce many assets or variants by issuing one image_gen call per requested asset or variant.
• In the CLI fallback path, use the CLI generate-batch subcommand only when the user explicitly chose CLI mode and needs many prompts/assets.
• For many distinct assets, do not use n as a substitute for separate prompts. n is for variants of one prompt; distinct assets need distinct built-in calls or distinct CLI generate-batch jobs.

Assume the user wants a new image unless they clearly ask to change an existing one.

Workflow

1. Decide the top-level mode: built-in by default, including transparent-output requests; fallback CLI only if explicitly requested or confirmed.
2. Decide the intent: generate or edit.
3. Decide whether the output is preview-only or meant to be consumed by the current project.
4. Decide the execution strategy: single asset vs repeated built-in calls vs CLI generate-batch.
5. Collect inputs up front: prompt(s), exact text (verbatim), constraints/avoid list, and any input images.
6. For every input image, label its role explicitly:

• reference image
• edit target
• supporting insert/style/compositing input

1. If the edit target is only on the local filesystem and you are staying on the built-in path, inspect it with view_image first so the image is available in conversation context.
2. If the user asked for a photo, illustration, sprite, product image, banner, or other explicitly raster-style asset, use image_gen rather than substituting SVG/HTML/CSS placeholders. If the request is for an icon, logo, or UI graphic that should match existing repo-native SVG/vector/code assets, prefer editing those directly instead.
3. Augment the prompt based on specificity:

• If the user's prompt is already specific and detailed, normalize it into a clear spec without adding creative requirements.
• If the user's prompt is generic, add tasteful augmentation only when it materially improves output quality.

1. Use the built-in image_gen tool by default.
2. For transparent-output requests, ask built-in image_gen for a transparent background and preserve the generated alpha channel.
3. Inspect outputs and validate: subject, style, composition, text accuracy, and invariants/avoid items.
4. Iterate with a single targeted change, then re-check.
5. For preview-only work, render the image inline; the underlying file may remain at the default $CODEXHOME/generatedimages/... path.
6. For project-bound work, move or copy the selected artifact into the workspace and update any consuming code or references. Never leave a project-referenced asset only at the default $CODEXHOME/generatedimages/... path.
7. For batches or multi-asset requests, persist every requested deliverable final in the workspace unless the user explicitly asked to keep outputs preview-only. Discarded variants do not need to be kept unless requested.
8. If the user explicitly chooses or confirms the CLI fallback, then use the fallback-only docs for model, quality, size, input_fidelity, masks, output format, output paths, and network setup.
9. Always report the final saved path(s) for any workspace-bound asset(s), plus the final prompt or prompt set and whether the built-in tool or fallback CLI mode was used.

Transparent image requests

Ask built-in image_gen for a genuinely transparent background and preserve its alpha.

Prompt augmentation

Reformat user prompts into a structured, production-oriented spec. Make the user's goal clearer and more actionable, but do not blindly add detail.

Treat this as prompt-shaping guidance, not a closed schema. Use only the lines that help, and add a short extra labeled line when it materially improves clarity.

Specificity policy

Use the user's prompt specificity to decide how much augmentation is appropriate:

• If the prompt is already specific and detailed, preserve that specificity and only normalize/structure it.
• If the prompt is generic, you may add tasteful augmentation when it will materially improve the result.

Allowed augmentations:

• composition or framing hints
• polish level or intended-use hints
• practical layout guidance
• reasonable scene concreteness that supports the stated request

Not allowed augmentations:

• extra characters or objects that are not implied by the request
• brand names, slogans, palettes, or narrative beats that are not implied
• arbitrary side-specific placement unless the surrounding layout supports it

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 and wireframes; specify the desired fidelity.
• infographic-diagram — diagrams/infographics with structured layout and text.
• scientific-educational — classroom explainers, scientific diagrams, and learning visuals with required labels and accuracy constraints.
• ads-marketing — campaign concepts and ad creatives with audience, brand position, scene, and exact tagline/copy.
• productivity-visual — slide, chart, workflow, and data-heavy business visuals.
• 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 (including interior swaps).
• lighting-weather — time-of-day/season/atmosphere changes only.
• background-extraction — transparent background / clean cutout. Ask built-in image_gen for actual transparency.
• 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.

Shared prompt schema

Use the following labeled spec as shared prompt scaffolding for both top-level modes:

Use case: <taxonomy slug>
Asset type: <where the asset will be used>
Primary request: <user's main prompt>
Input images: <Image 1: role; Image 2: role> (optional)
Scene/backdrop: <environment>
Subject: <main subject>
Style/medium: <photo/illustration/3D/etc>
Composition/framing: <wide/close/top-down; placement>
Lighting/mood: <lighting + mood>
Color palette: <palette notes>
Materials/textures: <surface details>
Text (verbatim): "<exact text>"
Constraints: <must keep/must avoid>
Avoid: <negative constraints>

Notes:

• Asset type and Input images are prompt scaffolding, not dedicated CLI flags.
• Scene/backdrop refers to the visual setting. It is not the same as the fallback CLI background parameter, which controls output transparency behavior.
• Fallback-only execution notes such as Quality:, Input fidelity:, masks, output format, and output paths belong in the CLI path only. Do not treat them as built-in image_gen tool arguments.

Augmentation rules: