Imagegen

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 image_gen tool for normal image generation, editing, and simple transparent-image requests. Does not require OPENAI_API_KEY.
• Fallback CLI mode: scripts/image_gen.py CLI. Use when the user explicitly asks for the CLI/API/model path, or after the user explicitly confirms a true model-native transparency fallback with gpt-image-1.5. Requires OPENAI_API_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.
• If the user explicitly asks for a transparent image/background, stay on built-in image_gen first: prompt for a flat removable chroma-key background, then remove it locally with the installed helper at $CODEX_HOME/skills/.system/imagegen/scripts/remove_chroma_key.py.
• Never silently switch from built-in image_gen or CLI gpt-image-2 to CLI gpt-image-1.5. Treat this as a model/path downgrade and ask the user before doing it, unless the user has already explicitly requested gpt-image-1.5, scripts/image_gen.py, or CLI fallback.
• If a transparent request appears too complex for clean chroma-key removal, asks for true/native transparency, or local removal fails validation, explain that true transparency requires CLI gpt-image-1.5 --background transparent --output-format png because gpt-image-2 does not support background=transparent, then ask whether to proceed. Run the CLI fallback only after the user confirms.
• 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 OPENAI_API_KEY. 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 $CODEX_HOME/* 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 image_gen tool. If a specific location is needed, generate first and then move or copy the selected output from $CODEX_HOME/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 $CODEX_HOME/* path.

• Never leave a project-referenced asset only at the default $CODEX_HOME/* 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

Local post-processing helper:

• $CODEX_HOME/skills/.system/imagegen/scripts/remove_chroma_key.py: removes a flat chroma-key background from a generated image and writes a PNG/WebP with alpha. Prefer auto-key sampling, soft matte, and despill for antialiased edges.

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 simple transparent-output requests; fallback CLI only if explicitly requested or after the user explicitly confirms a transparent-output fallback. 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

7. 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. 8. 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. 9. 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.

10. Use the built-in image_gen tool by default. 11. For transparent-output requests, follow the transparent image guidance below: generate with built-in image_gen on a flat chroma-key background, copy the selected output into the workspace or tmp/imagegen/, run the installed $CODEX_HOME/skills/.system/imagegen/scripts/remove_chroma_key.py helper, and validate the alpha result before using it. If this path looks unsuitable or fails, ask before switching to CLI gpt-image-1.5. 12. Inspect outputs and validate: subject, style, composition, text accuracy, and invariants/avoid items. 13. Iterate with a single targeted change, then re-check. 14. For preview-only work, render the image inline; the underlying file may remain at the default $CODEX_HOME/generated_images/... path. 15. 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 $CODEX_HOME/generated_images/... path. 16. 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. 17. 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. 18. 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

Transparent-image requests still use built-in image_gen first. Because the built-in tool does not expose a true transparent-background control, create a removable chroma-key source image and then convert the key color to alpha locally.

Default sequence: 1. Use built-in image_gen to generate the requested subject on a perfectly flat solid chroma-key background. 2. Choose a key color that is unlikely to appear in the subject: default #00ff00, use #ff00ff for green subjects, and avoid #0000ff for blue subjects. 3. After generation, move or copy the selected source image from $CODEX_HOME/generated_images/... into the workspace or tmp/imagegen/. 4. Run the installed helper path, not a project-relative script path:

   python "${CODEX_HOME:-$HOME/.codex}/skills/.system/imagegen/scripts/remove_chroma_key.py" \
     --input <source> \
     --out <final.png> \
     --auto-key border \
     --soft-matte \
     --transparent-threshold 12 \
     --opaque-threshold 220 \
     --despill

5. Validate that the output has an alpha channel, transparent corners, plausible subject coverage, and no obvious key-color fringe. If a thin fringe remains, retry once with --edge-contract 1; use --edge-feather 0.25 only when the edge is visibly stair-stepped and the subject is not shiny or reflective. 6. Save the final alpha PNG/WebP in the project if the asset is project-bound. Never leave a project-referenced transparent asset only under $CODEX_HOME/*.

Prompt transparent requests like this:

Create the requested subject on a perfectly flat solid #00ff00 chroma-key background for background removal.
The background must be one uniform color with no shadows, gradients, texture, reflections, floor plane, or lighting variation.
Keep the subject fully separated from the background with crisp edges and generous padding.
Do not use #00ff00 anywhere in the subject.
No cast shadow, no contact shadow, no reflection, no watermark, and no text unless explicitly requested.

Do not automatically use CLI gpt-image-1.5 --background transparent --output-format png instead of chroma keying. Ask the user first when the user asks for true/native transparency, when local removal fails validation, or when the requested image is complex: hair, fur, feathers, smoke, glass, liquids, translucent materials, reflective objects, soft shadows, realistic product grounding, or subject colors that conflict with all practical key colors.

Use a concise confirmation like:

This likely needs true native transparency. The default built-in path uses a chroma-key background plus local removal, but true transparency requires the CLI fallback with gpt-image-1.5 because gpt-image-2 does not support background=transparent. It also requires OPENAI_API_KEY. Should I proceed with that CLI fallback?

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. Use built-in image_gen with chroma-key removal first for simple opaque subjects; ask before using CLI true transparency for complex subjects.
• 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:

• Keep it short.
• Add only the details needed to improve the prompt materially.
• For edits, explicitly list invariants (change only X; keep Y unchanged).
• If any critical detail is missing and blocks success, ask a question; otherwise proceed.

Examples

Generation example (hero image)

Use case: product-mockup
Asset type: landing page hero
Primary request: a minimal hero image of a ceramic coffee mug
Style/medium: clean product photography
Composition/framing: wide composition with usable negative space for page copy if needed
Lighting/mood: soft studio lighting
Constraints: no logos, no text, no watermark

Edit example (invariants)

Use case: precise-object-edit
Asset type: product photo background replacement
Primary request: replace only the background with a warm sunset gradient
Constraints: change only the background; keep the product and its edges unchanged; no text; no watermark

Prompting best practices

• Structure prompt as scene/backdrop -> subject -> details -> constraints.
• Include intended use (ad, UI mock, infographic) to set the mode and polish level.
• Use camera/composition language for photorealism.
• Only use SVG/vector stand-ins when the user explicitly asked for vector output or a non-image placeholder.
• Quote exact text and specify typography + placement.
• For tricky words, spell them letter-by-letter and require verbatim rendering.
• For multi-image inputs, reference images by index and describe how they should be used.
• For edits, repeat invariants every iteration to reduce drift.
• Iterate with single-change follow-ups.
• If the prompt is generic, add only the extra detail that will materially help.
• If the prompt is already detailed, normalize it instead of expanding it.
• For CLI fallback only, see references/cli.md and references/image-api.md for model, quality, input_fidelity, masks, output format, and output-path guidance.
• For transparent images, use the built-in-first chroma-key workflow unless the request is complex enough to need true CLI transparency; ask before switching to CLI gpt-image-1.5.

More principles shared by both modes: references/prompting.md. Copy/paste specs shared by both modes: references/sample-prompts.md.

Guidance by asset type

Asset-type templates (website assets, game assets, wireframes, logo) are consolidated in references/sample-prompts.md.

gpt-image-2 guidance for CLI fallback

The fallback CLI defaults to gpt-image-2.

• Use gpt-image-2 for new CLI/API workflows unless the request needs true model-native transparent output.
• If a transparent request may need CLI fallback, ask before using gpt-image-1.5 unless the user already explicitly requested gpt-image-1.5, scripts/image_gen.py, or CLI fallback. Explain that the built-in chroma-key path is the default, but true transparency requires gpt-image-1.5 because gpt-image-2 does not support background=transparent.
• gpt-image-2 always uses high fidelity for image inputs; do not set input_fidelity with this model.
• gpt-image-2 supports quality values low, medium, high, and auto.
• Use quality low for fast drafts, thumbnails, and quick iterations. Use medium, high, or auto for final assets, dense text, diagrams, identity-sensitive edits, or high-resolution outputs.
• Square images are typically fastest to generate. Use 1024x1024 for fast square drafts.
• If the user asks for 4K-style output, use 3840x2160 for landscape or 2160x3840 for portrait.
• gpt-image-2 size may be auto or WIDTHxHEIGHT if all constraints hold: max edge <= 3840px, both edges multiples of 16px, long-to-short ratio <= 3:1, total pixels between 655,360 and 8,294,400.

Popular gpt-image-2 sizes:

• 1024x1024 square
• 1536x1024 landscape
• 1024x1536 portrait
• 2048x2048 2K square
• 2048x1152 2K landscape
• 3840x2160 4K landscape
• 2160x3840 4K portrait
• auto

Fallback CLI mode only

Temp and output conventions

These conventions apply only to the CLI fallback. They do not describe built-in image_gen output behavior.

• Use tmp/imagegen/ for intermediate files (for example JSONL batches); delete them when done.
• Write final artifacts under output/imagegen/.
• Use --out or --out-dir to control output paths; keep filenames stable and descriptive.

Dependencies

Prefer uv for dependency management in this repo.

Required Python package:

uv pip install openai

Required for local chroma-key removal and optional downscaling:

uv pip install pillow

Portability note:

• If you are using the installed skill outside this repo, install dependencies into that environment with its package manager.
• In uv-managed environments, uv pip install ... remains the preferred path.

Environment

• OPENAI_API_KEY must be set for live API calls.
• Do not ask the user for OPENAI_API_KEY when using the built-in image_gen tool.
• Never ask the user to paste the full key in chat. Ask them to set it locally and confirm when ready.

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.

If installation is not possible in this environment, tell the user which dependency is missing and how to install it into their active environment.

Script-mode notes

• CLI commands + examples: references/cli.md
• API parameter quick reference: references/image-api.md
• Network approvals / sandbox settings for CLI mode: references/codex-network.md

Reference map

• references/prompting.md: shared prompting principles for both modes.
• references/sample-prompts.md: shared copy/paste prompt recipes for both modes.
• references/cli.md: fallback-only CLI usage via scripts/image_gen.py.
• references/image-api.md: fallback-only API/CLI parameter reference.
• references/codex-network.md: fallback-only network/sandbox troubleshooting for CLI mode.
• scripts/image_gen.py: fallback-only CLI implementation. Do not load or use it unless the user explicitly chooses CLI mode or explicitly confirms a transparent request's true CLI transparency fallback.
• $CODEX_HOME/skills/.system/imagegen/scripts/remove_chroma_key.py: local post-processing helper for built-in transparent-image requests.

CLI reference (scripts/image_gen.py)

This file is for the fallback CLI mode only. Read it when the user explicitly asks to use scripts/image_gen.py / CLI / API / model controls, or after the user explicitly confirms that a transparent-output request should use the gpt-image-1.5 true-transparency fallback path.

generate-batch is a CLI subcommand in this fallback path. It is not a top-level mode of the skill. The word batch in a user request is not CLI opt-in by itself.

What this CLI does

• generate: generate a new image from a prompt
• edit: edit one or more existing images
• generate-batch: run many generation jobs from a JSONL file after the user explicitly chooses CLI/API/model controls

Real API calls require network access + OPENAI_API_KEY. --dry-run does not.

Quick start (works from any repo)

Set a stable path to the skill CLI (default CODEX_HOME is ~/.codex):

export CODEX_HOME="${CODEX_HOME:-$HOME/.codex}"
export IMAGE_GEN="$CODEX_HOME/skills/.system/imagegen/scripts/image_gen.py"

Install dependencies into that environment with its package manager. In uv-managed environments, uv pip install ... remains the preferred path.

Quick start

Dry-run (no API call; no network required; does not require the openai package):

python "$IMAGE_GEN" generate \
  --prompt "Test" \
  --out output/imagegen/test.png \
  --dry-run

Notes:

• One-off dry-runs print the API payload and the computed output path(s).
• Repo-local finals should live under output/imagegen/.

Generate (requires OPENAI_API_KEY + network):

python "$IMAGE_GEN" generate \
  --prompt "A cozy alpine cabin at dawn" \
  --size 1024x1024 \
  --out output/imagegen/alpine-cabin.png

Edit:

python "$IMAGE_GEN" edit \
  --image input.png \
  --prompt "Replace only the background with a warm sunset" \
  --out output/imagegen/sunset-edit.png

Guardrails

• Use the bundled CLI directly (python "$IMAGE_GEN" ...) after activating the correct environment.
• Do not create one-off runners (for example gen_images.py) unless the user explicitly asks for a custom wrapper.
• Never modify scripts/image_gen.py. If something is missing, ask the user before doing anything else.
• Do not silently downgrade from CLI gpt-image-2 or built-in image_gen to CLI gpt-image-1.5; ask first unless the user already explicitly requested gpt-image-1.5, scripts/image_gen.py, or CLI fallback.

Defaults

• Model: gpt-image-2
• Supported model family for this CLI: GPT Image models (gpt-image-*)
• Size: auto
• Quality: medium
• Output format: png
• Default one-off output path: output/imagegen/output.png
• Background: unspecified unless --background is set

gpt-image-2 size and model guidance

gpt-image-2 is the default model for new CLI fallback work.

• Use --quality low for fast drafts, thumbnails, and quick iterations.
• Use --quality medium, --quality high, or --quality auto for final assets, dense text, diagrams, identity-sensitive edits, and high-resolution outputs.
• Square images are typically fastest. Use --size 1024x1024 for quick square drafts.
• If the user asks for 4K-style output, use --size 3840x2160 for landscape or --size 2160x3840 for portrait.
• Do not pass --input-fidelity with gpt-image-2; this model always uses high fidelity for image inputs.
• Do not use --background transparent with gpt-image-2; the default transparent-image workflow uses built-in image_gen on a flat chroma-key background plus local removal. Use gpt-image-1.5 only after the user explicitly confirms the true-transparent CLI fallback, unless they already requested gpt-image-1.5, scripts/image_gen.py, or CLI fallback.

Popular gpt-image-2 sizes:

• 1024x1024
• 1536x1024
• 1024x1536
• 2048x2048
• 2048x1152
• 3840x2160
• 2160x3840
• auto

gpt-image-2 size constraints:

• max edge <= 3840px
• both edges multiples of 16px
• long edge to short edge ratio <= 3:1
• total pixels between 655,360 and 8,294,400
• outputs above 2560x1440 total pixels are experimental

Fast draft:

python "$IMAGE_GEN" generate \
  --prompt "A product thumbnail of a matte ceramic mug on a stone surface" \
  --quality low \
  --size 1024x1024 \
  --out output/imagegen/mug-draft.png

Final 2K landscape:

python "$IMAGE_GEN" generate \
  --prompt "A polished landing-page hero image of a matte ceramic mug on a stone surface" \
  --quality high \
  --size 2048x1152 \
  --out output/imagegen/mug-hero.png

4K landscape:

python "$IMAGE_GEN" generate \
  --prompt "A detailed architectural visualization at golden hour" \
  --size 3840x2160 \
  --quality high \
  --out output/imagegen/architecture-4k.png

True transparent fallback request:

Ask for confirmation before using this command unless the user already explicitly requested gpt-image-1.5, scripts/image_gen.py, or CLI fallback.

python "$IMAGE_GEN" generate \
  --model gpt-image-1.5 \
  --prompt "A clean product cutout on a transparent background" \
  --background transparent \
  --output-format png \
  --out output/imagegen/product-cutout.png

When using this path, explain briefly that built-in image_gen plus chroma-key removal is the default transparent-image path, but this request needs true model-native transparency. gpt-image-2 does not support background=transparent, so gpt-image-1.5 is required for this confirmed fallback.

Quality, input fidelity, and masks (CLI fallback only)

These are explicit CLI controls. They are not built-in image_gen tool arguments.

• --quality works for generate, edit, and generate-batch: low|medium|high|auto
• --input-fidelity is edit-only and validated as low|high; it is not supported for gpt-image-2
• --mask is edit-only

Example:

python "$IMAGE_GEN" edit \
  --model gpt-image-1.5 \
  --image input.png \
  --prompt "Change only the background" \
  --quality high \
  --input-fidelity high \
  --out output/imagegen/background-edit.png

Mask notes:

• For multi-image edits, pass repeated --image flags. Their order is meaningful, so describe each image by index and role in the prompt.
• The CLI accepts a single --mask.
• Image and mask must be the same size and format and each under 50MB.
• Masks must include an alpha channel.
• If multiple input images are provided, the mask applies to the first image.
• Masking is prompt-guided; do not promise exact pixel-perfect mask boundaries.
• Use a PNG mask when possible; the script treats mask handling as best-effort and does not perform full preflight validation beyond file checks/warnings.
• In the edit prompt, repeat invariants (change only the background; keep the subject unchanged) to reduce drift.

Output handling

• Use tmp/imagegen/ for temporary JSONL inputs or scratch files.
• Use output/imagegen/ for final outputs.
• Reruns fail if a target file already exists unless you pass --force.
• --out-dir changes one-off naming to image_1.<ext>, image_2.<ext>, and so on.
• Downscaled copies use the default suffix -web unless you override it.

Common recipes

Generate with augmentation fields:

python "$IMAGE_GEN" generate \
  --prompt "A minimal hero image of a ceramic coffee mug" \
  --use-case "product-mockup" \
  --style "clean product photography" \
  --composition "wide product shot with usable negative space for page copy" \
  --constraints "no logos, no text" \
  --out output/imagegen/mug-hero.png

Generate + also write a downscaled copy for fast web loading:

python "$IMAGE_GEN" generate \
  --prompt "A cozy alpine cabin at dawn" \
  --size 1024x1024 \
  --downscale-max-dim 1024 \
  --out output/imagegen/alpine-cabin.png

Generate multiple prompts concurrently (async batch):

mkdir -p tmp/imagegen output/imagegen/batch
cat > tmp/imagegen/prompts.jsonl << 'EOF'
{"prompt":"Cavernous hangar interior with a compact shuttle parked near the center","use_case":"stylized-concept","composition":"wide-angle, low-angle","lighting":"volumetric light rays through drifting fog","constraints":"no logos or trademarks; no watermark","size":"1536x1024"}
{"prompt":"Gray wolf in profile in a snowy forest","use_case":"photorealistic-natural","composition":"eye-level","constraints":"no logos or trademarks; no watermark","size":"1024x1024"}
EOF

python "$IMAGE_GEN" generate-batch \
  --input tmp/imagegen/prompts.jsonl \
  --out-dir output/imagegen/batch \
  --concurrency 5

rm -f tmp/imagegen/prompts.jsonl

Notes:

• generate-batch requires --out-dir.
• generate-batch requires --out-dir.
• Use --concurrency to control parallelism (default 5).
• Per-job overrides are supported in JSONL (for example size, quality, background, output_format, output_compression, moderation, n, model, out, and prompt-augmentation fields).
• --n generates multiple variants for a single prompt; generate-batch is for many different prompts.
• In batch mode, per-job out is treated as a filename under --out-dir.
• For many requested deliverable assets, provide one prompt/job per distinct asset and use semantic filenames when possible.

CLI notes

• Supported sizes depend on the model. gpt-image-2 supports flexible constrained sizes; older GPT Image models support 1024x1024, 1536x1024, 1024x1536, or auto.
• True transparent CLI outputs require output_format to be png or webp and are not supported by gpt-image-2.
• --prompt-file, --output-compression, --moderation, --max-attempts, --fail-fast, --force, and --no-augment are supported.
• This CLI is intended for GPT Image models. Do not assume older non-GPT image-model behavior applies here.

See also

• API parameter quick reference for fallback CLI mode: references/image-api.md
• Prompt examples shared across both top-level modes: references/sample-prompts.md
• Network/sandbox notes for fallback CLI mode: references/codex-network.md
• Built-in-first transparent image workflow: SKILL.md and $CODEX_HOME/skills/.system/imagegen/scripts/remove_chroma_key.py

Codex network approvals / sandbox notes

This file is for the fallback CLI mode only. Read it when the user explicitly asks to use scripts/image_gen.py / CLI / API / model controls, or after the user explicitly confirms that a transparent-output request should use the gpt-image-1.5 true-transparency fallback path.

This guidance is intentionally isolated from SKILL.md because it can vary by environment and may become stale. Prefer the defaults in your environment when in doubt.

Why am I asked to approve image generation calls?

The fallback CLI uses the OpenAI Image API, so it needs outbound network access. In many Codex setups, network access is disabled by default and/or the approval policy requires confirmation before networked commands run.

Important note about approvals vs network

• --ask-for-approval never suppresses approval prompts.
• It does not by itself enable network access.
• In workspace-write, network access still depends on your Codex configuration (for example [sandbox_workspace_write] network_access = true).

How do I reduce repeated approval prompts?

If you trust the repo and want fewer prompts, use a configuration or profile that both:

• enables network for the sandbox mode you plan to use
• sets an approval policy that matches your risk tolerance

Example ~/.codex/config.toml pattern:

approval_policy = "on-request"
sandbox_mode = "workspace-write"

[sandbox_workspace_write]
network_access = true

If you want quieter automation after network is enabled, you can choose a stricter approval policy, but do that intentionally and with care.

Safety note

Enabling network and reducing approvals lowers friction, but increases risk if you run untrusted code or work in an untrusted repository.

Image API quick reference

This file is for the fallback CLI mode only. Use it when the user explicitly asks to use scripts/image_gen.py / CLI / API / model controls, or after the user explicitly confirms that a transparent-output request should use the gpt-image-1.5 true-transparency fallback path.

These parameters describe the Image API and bundled CLI fallback surface. Do not assume they are normal arguments on the built-in image_gen tool.

Scope

• This fallback CLI is intended for GPT Image models (gpt-image-2, gpt-image-1.5, gpt-image-1, and gpt-image-1-mini).
• The built-in image_gen tool and the fallback CLI do not expose the same controls.

Model summary

gpt-image-2 sizes

gpt-image-2 accepts auto or any WIDTHxHEIGHT size that satisfies all constraints:

• Maximum edge length must be less than or equal to 3840px.
• Both edges must be multiples of 16px.
• Long edge to short edge ratio must not exceed 3:1.
• Total pixels must be at least 655,360 and no more than 8,294,400.

Popular sizes:

Square images are typically fastest to generate. For 4K-style output, use 3840x2160 or 2160x3840.

Endpoints

• Generate: POST /v1/images/generations (client.images.generate(...))
• Edit: POST /v1/images/edits (client.images.edit(...))

Core parameters for GPT Image models

• prompt: text prompt
• model: image model
• n: number of images (1-10)
• size: auto by default for gpt-image-2; flexible WIDTHxHEIGHT sizes are allowed only for gpt-image-2; older GPT Image models use 1024x1024, 1536x1024, 1024x1536, or auto
• quality: low, medium, high, or auto
• background: output transparency behavior (transparent, opaque, or auto) for generated output; this is not the same thing as the prompt's visual scene/backdrop
• output_format: png (default), jpeg, webp
• output_compression: 0-100 (jpeg/webp only)
• moderation: auto (default) or low

Edit-specific parameters

• image: one or more input images. For GPT Image models, you can provide up to 16 images.
• mask: optional mask image
• input_fidelity: low or high only for models that support it; do not set this for gpt-image-2

Model-specific note for input_fidelity:

• gpt-image-2 always uses high fidelity for image inputs and does not support setting input_fidelity.
• gpt-image-1 and gpt-image-1-mini preserve all input images, but the first image gets richer textures and finer details.
• gpt-image-1.5 preserves the first 5 input images with higher fidelity.

Transparent backgrounds

gpt-image-2 does not currently support the Image API background=transparent parameter. The skill's default transparent-image path is built-in image_gen with a flat chroma-key background, followed by local alpha extraction with python "${CODEX_HOME:-$HOME/.codex}/skills/.system/imagegen/scripts/remove_chroma_key.py".

Use CLI gpt-image-1.5 with background=transparent and a transparent-capable output format such as png or webp only after the user explicitly confirms that fallback, unless they already requested gpt-image-1.5, scripts/image_gen.py, or CLI fallback. If the user asks for true/native transparency, the subject is too complex for clean chroma-key removal, or local background removal fails validation, explain the tradeoff and ask before switching.

Output

• data[] list with b64_json per image
• The bundled scripts/image_gen.py CLI decodes b64_json and writes output files for you.

Limits and notes

• Input images and masks must be under 50MB.
• Use the edits endpoint when the user requests changes to an existing image.
• Masking is prompt-guided; exact shapes are not guaranteed.
• Large sizes and high quality increase latency and cost.
• Use quality=low for fast drafts, thumbnails, and quick iterations. Use medium or high for final assets, dense text, diagrams, identity-sensitive edits, or high-resolution outputs.
• High input_fidelity can materially increase input token usage on models that support it.
• If a request fails because a specific option is unsupported by the selected GPT Image model, retry manually without that option only when the option is not required by the user. If true transparent CLI output is required, ask before switching to gpt-image-1.5 instead of dropping background=transparent, unless the user already explicitly chose that fallback.

Important boundary

• quality, input_fidelity, explicit masks, background, output_format, and related parameters are fallback-only execution controls.
• Do not assume they are built-in image_gen tool arguments.

Prompting best practices

These prompting principles are shared by both top-level modes of the skill:

• built-in image_gen tool (default)
• explicit scripts/image_gen.py CLI fallback

This file is about prompt structure, specificity, and iteration. Fallback-only execution controls such as quality, input_fidelity, masks, output format, and output paths live in the fallback docs.

Contents

• Structure
• Specificity policy
• Allowed and disallowed augmentation
• Composition and layout
• Constraints and invariants
• Text in images
• Input images and references
• Iterate deliberately
• Transparent images
• Fallback-only execution controls
• Use-case tips
• Where to find copy/paste recipes

Structure

• Use a consistent order: scene/backdrop -> subject -> key details -> constraints -> output intent.
• Include intended use (ad, UI mock, infographic) to set the level of polish.
• For complex requests, use short labeled lines instead of one long paragraph.

Specificity policy

• If the user prompt is already specific and detailed, normalize it into a clean spec without adding creative requirements.
• If the prompt is generic, you may add tasteful detail when it materially improves the output.
• Treat examples in sample-prompts.md as fully-authored recipes, not as the default amount of augmentation to add to every request.
• For photorealism, include photorealistic directly when that is the goal, plus concrete real-world texture such as pores, wrinkles, fabric wear, material grain, or imperfect everyday detail.

Allowed and disallowed augmentation

Allowed augmentation for generic prompts:

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

Do not add:

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

Composition and layout

• Specify framing and viewpoint (close-up, wide, top-down) and placement only when it materially helps.
• Call out negative space if the asset clearly needs room for UI or copy.
• Avoid making left/right layout decisions unless the user or surrounding layout supports them.
• For people, describe body framing, scale, gaze, and object interactions when they matter (full body visible, looking down at the book, hands naturally gripping the handlebars).

Constraints and invariants

• State what must not change (keep background unchanged).
• For edits, say change only X; keep Y unchanged and repeat invariants on every iteration to reduce drift.

Text in images

• Put literal text in quotes or ALL CAPS and specify typography (font style, size, color, placement).
• Spell uncommon words letter-by-letter if accuracy matters.
• For in-image copy, require verbatim rendering and no extra characters.
• In CLI fallback mode, use medium or high quality for small text, dense infographics, data-heavy slides, multi-font layouts, legends, axes, and footnotes.

Input images and references

• Do not assume that every provided image is an edit target.
• Label each image by index and role (Image 1: edit target, Image 2: style reference).
• If the user provides images for style, composition, or mood guidance and does not ask to modify them, treat the request as generation with references.
• If the user asks to preserve an existing image while changing specific parts, treat the request as an edit.
• For compositing, describe how the images interact (place the subject from Image 2 into Image 1).

Iterate deliberately

• Start with a clean base prompt, then make small single-change edits.
• Re-specify critical constraints when you iterate.
• Prefer one targeted follow-up at a time over rewriting the whole prompt.

Transparent images

• Use built-in image_gen first for transparent-image requests. If the subject is clearly too complex for chroma-key removal, explain the fallback and ask before switching to CLI.
• Prompt for a perfectly flat solid chroma-key background, usually #00ff00; use #ff00ff when the subject is green, and avoid key colors that appear in the subject.
• Explicitly prohibit shadows, gradients, floor planes, reflections, texture, and lighting variation in the background.
• Ask for crisp edges, generous padding, and no use of the key color inside the subject.
• After generation, remove the background locally with python "${CODEX_HOME:-$HOME/.codex}/skills/.system/imagegen/scripts/remove_chroma_key.py" --input <source> --out <final.png> --auto-key border --soft-matte --transparent-threshold 12 --opaque-threshold 220 --despill and validate the alpha result before shipping it.
• Use soft matte and despill for antialiased edges; hard tolerance-only removal is mainly for flat pixel-art or exact-color fixtures.
• Use CLI gpt-image-1.5 --background transparent --output-format png only after the user explicitly confirms the fallback, or when the user already explicitly requested gpt-image-1.5, scripts/image_gen.py, or CLI fallback. Ask first for true/native transparency requests, failed chroma-key validation, or complex transparent subjects such as hair, fur, glass, smoke, liquids, translucent materials, reflective objects, or soft shadows.

Fallback-only execution controls

• quality, input_fidelity, explicit masks, output format, and output paths are fallback-only execution controls.
• Do not assume they are built-in image_gen tool arguments.
• If the user explicitly chooses CLI fallback, see references/cli.md and references/image-api.md for those controls.
• In CLI fallback mode, gpt-image-2 is the default. It supports quality=low|medium|high|auto; use low for fast drafts and thumbnails, and move to medium, high, or auto for final assets.
• gpt-image-2 always uses high fidelity for image inputs, so do not set input_fidelity with that model.
• If a transparent request needs true CLI transparency, ask before using gpt-image-1.5 unless the user already explicitly chose it. Explain that built-in chroma-key removal is the default path, but gpt-image-2 does not support background=transparent.
• If the user asks for 4K-style output with gpt-image-2, use 3840x2160 for landscape or 2160x3840 for portrait.

Use-case tips

Generate:

• photorealistic-natural: Prompt as if a real photo is captured in the moment; use photography language (lens, lighting, framing); call for real texture; avoid over-stylized polish unless requested.
• product-mockup: Describe the product/packaging and materials; ensure clean silhouette and label clarity; if in-image text is needed, require verbatim rendering and specify typography.
• ui-mockup: Describe the target fidelity first (shippable mockup or low-fi wireframe), then focus on layout, hierarchy, and practical UI elements; avoid concept-art language.
• infographic-diagram: Define the audience and layout flow; label parts explicitly; require verbatim text; prefer higher quality in CLI mode for dense labels.
• logo-brand: Keep it simple and scalable; ask for a strong silhouette and balanced negative space; avoid decorative flourishes unless requested.
• ads-marketing: Write like a creative brief; include brand positioning, audience, desired vibe, scene, and exact tagline if text must appear.
• productivity-visual: Name the exact artifact (slide, chart, workflow diagram), define the canvas and hierarchy, provide real labels/data, and ask for readable typography and polished spacing.
• scientific-educational: Define audience, lesson objective, required labels, scientific constraints, arrows, and scan-friendly whitespace.
• illustration-story: Define panels or scene beats; keep each action concrete.
• stylized-concept: Specify style cues, material finish, and rendering approach (3D, painterly, clay) without inventing new story elements.
• historical-scene: State the location/date and required period accuracy; constrain clothing, props, and environment to match the era.

Edit:

• text-localization: Change only the text; preserve layout, typography, spacing, and hierarchy; no extra words or reflow unless needed.
• identity-preserve: Lock identity (face, body, pose, hair, expression); change only the specified elements; match lighting and shadows.
• precise-object-edit: Specify exactly what to remove/replace; preserve surrounding texture and lighting; keep everything else unchanged.
• lighting-weather: Change only environmental conditions (light, shadows, atmosphere, precipitation); keep geometry, framing, and subject identity.
• background-extraction: For simple opaque subjects, request a clean cutout on a perfectly flat chroma-key background; crisp silhouette; generous padding; no shadows; no halos; preserve label text exactly; no restyling. Ask before using true CLI transparency for complex subjects.
• style-transfer: Specify style cues to preserve (palette, texture, brushwork) and what must change; add no extra elements to prevent drift.
• compositing: Reference inputs by index; specify what moves where; match lighting, perspective, and scale; keep the base framing unchanged.
• sketch-to-render: Preserve layout, proportions, and perspective; choose materials and lighting that support the supplied sketch without adding new elements.

Where to find copy/paste recipes

For copy/paste prompt specs (examples only), see references/sample-prompts.md. This file focuses on principles, specificity, and iteration patterns.

Sample prompts (copy/paste)

These prompt recipes are shared across both top-level modes of the skill:

• built-in image_gen tool (default)
• scripts/image_gen.py CLI fallback for explicit CLI/API/model requests or user-confirmed true-transparent-output fallback requests

Use these as starting points. They are intentionally complete prompt recipes, not the default amount of augmentation to add to every user request.

When adapting a user's prompt:

• keep user-provided requirements
• only add detail according to the specificity policy in SKILL.md
• do not treat every example below as permission to invent extra story elements

The labeled lines are prompt scaffolding, not a closed schema. Asset type and Input images are prompt-only scaffolding; the CLI does not expose them as dedicated flags.

Execution details such as explicit CLI flags, quality, input_fidelity, masks, output formats, and local output paths depend on mode. Use the built-in tool by default, including simple transparent-image requests. For transparent images, prompt for a flat chroma-key background and remove it locally with python "${CODEX_HOME:-$HOME/.codex}/skills/.system/imagegen/scripts/remove_chroma_key.py"; only apply CLI-specific controls when the user explicitly opts into fallback mode or explicitly confirms that the transparent request should use true CLI transparency.

CLI model notes:

• gpt-image-2 is the fallback CLI default for new workflows.
• gpt-image-2 supports quality values low, medium, high, and auto.
• For 4K-style gpt-image-2 output, use 3840x2160 or 2160x3840.
• If transparent output needs true CLI fallback, ask before using gpt-image-1.5 unless the user already explicitly requested gpt-image-1.5, scripts/image_gen.py, or CLI fallback. Explain that built-in chroma-key removal is the default path, but gpt-image-2 does not support background=transparent.
• Do not set input_fidelity with gpt-image-2; image inputs already use high fidelity.

For prompting principles (structure, specificity, invariants, iteration), see references/prompting.md.

Generate

photorealistic-natural

Use case: photorealistic-natural
Primary request: candid photo of an elderly sailor on a small fishing boat adjusting a net
Scene/backdrop: coastal water with soft haze
Subject: weathered skin with wrinkles and sun texture
Style/medium: photorealistic candid photo
Composition/framing: medium close-up, eye-level
Lighting/mood: soft coastal daylight, shallow depth of field, subtle film grain
Materials/textures: real skin texture, worn fabric, salt-worn wood
Constraints: natural color balance; no heavy retouching; no glamorization; no watermark
Avoid: studio polish; staged look

product-mockup

Use case: product-mockup
Primary request: premium product photo of a matte black shampoo bottle with a minimal label
Scene/backdrop: clean studio gradient from light gray to white
Subject: single bottle centered with subtle reflection
Style/medium: premium product photography
Composition/framing: centered, slight three-quarter angle, generous padding
Lighting/mood: softbox lighting, clean highlights, controlled shadows
Materials/textures: matte plastic, crisp label printing
Constraints: no logos or trademarks; no watermark

ui-mockup

Use case: ui-mockup
Primary request: mobile app home screen for a local farmers market with vendors and daily specials
Asset type: mobile app screen
Style/medium: realistic product UI, not concept art
Composition/framing: clean vertical mobile layout with clear hierarchy
Constraints: practical layout, clear typography, no logos or trademarks, no watermark

infographic-diagram

Use case: infographic-diagram
Primary request: detailed infographic of an automatic coffee machine flow
Scene/backdrop: clean, light neutral background
Subject: bean hopper -> grinder -> brew group -> boiler -> water tank -> drip tray
Style/medium: clean vector-like infographic with clear callouts and arrows
Composition/framing: vertical poster layout, top-to-bottom flow
Text (verbatim): "Bean Hopper", "Grinder", "Brew Group", "Boiler", "Water Tank", "Drip Tray"
Constraints: clear labels, strong contrast, no logos or trademarks, no watermark

scientific-educational

Use case: scientific-educational
Primary request: biology diagram titled "Cellular Respiration at a Glance" for high school students
Scene/backdrop: clean white classroom handout background
Subject: glucose turns into energy inside a cell; include glycolysis, Krebs cycle, and electron transport chain
Style/medium: flat scientific diagram with consistent icons, arrows, and readable labels
Composition/framing: landscape slide-style layout with clear hierarchy and generous whitespace
Text (verbatim): "Cellular Respiration at a Glance", "Glucose", "Pyruvate", "ATP", "NADH", "FADH2", "CO2", "O2", "H2O"
Constraints: scientifically plausible; avoid tiny text; no extra decoration; no watermark

logo-brand

Use case: logo-brand
Primary request: original logo for "Field & Flour", a local bakery
Style/medium: vector logo mark; flat colors; minimal
Composition/framing: single centered logo on a plain background with generous padding
Constraints: strong silhouette, balanced negative space; original design only; no gradients unless essential; no trademarks; no watermark

illustration-story

Use case: illustration-story
Primary request: 4-panel comic about a pet left alone at home
Scene/backdrop: cozy living room across panels
Subject: pet reacting to the owner leaving, then relaxing, then returning to a composed pose
Style/medium: comic illustration with clear panels
Composition/framing: 4 equal-sized vertical panels, readable actions per panel
Constraints: no text; no logos or trademarks; no watermark

stylized-concept

Use case: stylized-concept
Primary request: cavernous hangar interior with tall support beams and drifting fog
Scene/backdrop: industrial hangar interior, deep scale, light haze
Subject: compact shuttle parked near the center
Style/medium: cinematic concept art, industrial realism
Composition/framing: wide-angle, low-angle
Lighting/mood: volumetric light rays cutting through fog
Constraints: no logos or trademarks; no watermark

ads-marketing

Use case: ads-marketing
Primary request: campaign image for a streetwear brand called Thread
Subject: group of friends hanging out together in a stylish urban setting
Style/medium: polished youth streetwear campaign photography
Composition/framing: vertical ad layout with natural poses and integrated headline space
Lighting/mood: contemporary, energetic, tasteful
Text (verbatim): "Yours to Create."
Constraints: render the tagline exactly once; clean legible typography; no extra text; no watermarks; no unrelated logos

productivity-visual

Use case: productivity-visual
Primary request: one pitch-deck slide titled "Market Opportunity"
Asset type: fundraising slide image
Style/medium: clean modern deck slide, white background, crisp sans-serif typography
Subject: TAM/SAM/SOM concentric-circle diagram plus a small growth bar chart from 2021 to 2026
Composition/framing: 16:9 landscape slide, clear data hierarchy, polished spacing
Text (verbatim): "Market Opportunity", "TAM: $42B", "SAM: $8.7B", "SOM: $340M", "AGI Research, 2024", "Internal analysis"
Constraints: readable labels, no clip art, no stock photography, no decorative clutter, no watermark

historical-scene

Use case: historical-scene
Primary request: outdoor crowd scene in Bethel, New York on August 16, 1969
Scene/backdrop: open field with period-appropriate staging
Subject: crowd in period-accurate clothing, authentic environment
Style/medium: photorealistic photo
Composition/framing: wide shot, eye-level
Constraints: period-accurate details; no modern objects; no logos or trademarks; no watermark

Asset type templates (taxonomy-aligned)

Website assets template

Use case: <photorealistic-natural|stylized-concept|product-mockup|infographic-diagram|ui-mockup>
Asset type: <hero image / section illustration / blog header>
Primary request: <short description>
Scene/backdrop: <environment or abstract backdrop>
Subject: <main subject>
Style/medium: <photo/illustration/3D>
Composition/framing: <wide/centered; note usable negative space only if needed>
Lighting/mood: <soft/bright/neutral>
Color palette: <brand colors or neutral>
Constraints: <no text; no logos; no watermark; leave room for UI if needed>

Website assets example: minimal hero background

Use case: stylized-concept
Asset type: landing page hero background
Primary request: minimal abstract background with a soft gradient and subtle texture
Style/medium: matte illustration / soft-rendered abstract background
Composition/framing: wide composition with usable negative space for page copy
Lighting/mood: gentle studio glow
Color palette: restrained neutral palette
Constraints: no text; no logos; no watermark

Website assets example: feature section illustration

Use case: stylized-concept
Asset type: feature section illustration
Primary request: simple abstract shapes suggesting connection and flow
Scene/backdrop: subtle light-gray backdrop with faint texture
Style/medium: flat illustration; soft shadows; restrained contrast
Composition/framing: centered cluster; open margins for UI
Color palette: muted neutral palette
Constraints: no text; no logos; no watermark

Website assets example: blog header image

Use case: photorealistic-natural
Asset type: blog header image
Primary request: overhead desk scene with notebook, pen, and coffee cup
Scene/backdrop: warm wooden tabletop
Style/medium: photorealistic photo
Composition/framing: wide crop with clean room for page copy
Lighting/mood: soft morning light
Constraints: no text; no logos; no watermark

Game assets template

Use case: stylized-concept
Asset type: <game environment concept art / game character concept / game UI icon / tileable game texture>
Primary request: <biome/scene/character/icon/material>
Scene/backdrop: <location + set dressing> (if applicable)
Subject: <main focal element(s)>
Style/medium: <realistic/stylized>; <concept art / character render / UI icon / texture>
Composition/framing: <wide/establishing/top-down>; <camera angle>; <focal point placement>
Lighting/mood: <time of day>; <mood>; <volumetric/fog/etc>
Constraints: no logos or trademarks; no watermark

Game assets example: environment concept art

Use case: stylized-concept
Asset type: game environment concept art
Primary request: cavernous hangar interior with tall support beams and drifting fog
Scene/backdrop: industrial hangar interior, deep scale, light haze
Subject: compact shuttle parked near the center
Style/medium: cinematic concept art, industrial realism
Composition/framing: wide-angle, low-angle
Lighting/mood: volumetric light rays cutting through fog
Constraints: no logos or trademarks; no watermark

Game assets example: character concept

Use case: stylized-concept
Asset type: game character concept
Primary request: desert scout character with layered travel gear
Subject: long coat, satchel, practical travel clothing
Style/medium: character render; stylized realism
Composition/framing: neutral hero pose on a simple backdrop
Constraints: no logos or trademarks; no watermark

Game assets example: UI icon

Use case: stylized-concept
Asset type: game UI icon
Primary request: round shield icon with a subtle rune pattern
Style/medium: painted game UI icon
Composition/framing: centered icon; generous padding; clear silhouette
Constraints: no text; no background scene elements; no logos or trademarks; no watermark

Game assets example: tileable texture

Use case: stylized-concept
Asset type: tileable game texture
Primary request: worn sandstone blocks
Style/medium: seamless tileable texture; PBR-ish look
Scene/backdrop: neutral lighting reference only
Constraints: seamless edges; no obvious focal elements; no text; no logos or trademarks; no watermark

Wireframe template

Use case: ui-mockup
Asset type: website wireframe
Primary request: <page or flow to sketch>
Style/medium: low-fi grayscale wireframe
Composition/framing: <landscape or portrait to match expected device>
Subject: <sections in order; grid/columns; key labels>
Constraints: no color; no logos; no real photos; no watermark

Wireframe example: homepage (desktop)

Use case: ui-mockup
Asset type: website wireframe
Primary request: SaaS homepage layout with clear hierarchy
Style/medium: low-fi grayscale wireframe
Subject: top nav; hero with headline and CTA; three feature cards; testimonial strip; pricing preview; footer
Composition/framing: landscape desktop layout
Constraints: label major blocks; no color; no logos; no real photos; no watermark

Wireframe example: pricing page

Use case: ui-mockup
Asset type: website wireframe
Primary request: pricing page layout with comparison table
Style/medium: low-fi grayscale wireframe
Subject: header; plan toggle; 3 pricing cards; comparison table; FAQ accordion; footer
Composition/framing: desktop or tablet layout
Constraints: label key areas; no color; no logos; no real photos; no watermark

Wireframe example: mobile onboarding flow

Use case: ui-mockup
Asset type: mobile onboarding wireframe
Primary request: three-screen mobile onboarding flow
Style/medium: low-fi grayscale wireframe
Subject: screen 1 headline and CTA; screen 2 feature bullets; screen 3 form fields and CTA
Composition/framing: portrait mobile layout
Constraints: label screens and blocks; no color; no logos; no real photos; no watermark

Logo template

Use case: logo-brand
Asset type: logo concept
Primary request: <brand idea or symbol concept>
Style/medium: vector logo mark; flat colors; minimal
Composition/framing: centered mark; clear silhouette; generous margin
Color palette: <1-2 colors; high contrast>
Text (verbatim): "<exact name>" (only if needed)
Constraints: no gradients; no mockups; no 3D; no watermark

Logo example: abstract symbol mark

Use case: logo-brand
Asset type: logo concept
Primary request: geometric leaf symbol suggesting sustainability and growth
Style/medium: vector logo mark; flat colors; minimal
Composition/framing: centered mark; clear silhouette
Color palette: deep green and off-white
Constraints: no text unless requested; no gradients; no mockups; no 3D; no watermark

Logo example: monogram mark

Use case: logo-brand
Asset type: logo concept
Primary request: interlocking monogram of the letters "AV"
Style/medium: vector logo mark; flat colors; minimal
Composition/framing: centered mark; balanced spacing
Color palette: black on white
Constraints: no gradients; no mockups; no 3D; no watermark

Logo example: wordmark

Use case: logo-brand
Asset type: logo concept
Primary request: clean wordmark for a modern studio
Style/medium: vector wordmark; flat colors; minimal
Text (verbatim): "Studio North"
Composition/framing: centered text; even letter spacing
Constraints: no gradients; no mockups; no 3D; no watermark

Edit

text-localization

Use case: text-localization
Input images: Image 1: original infographic
Primary request: replace "Bean Hopper", "Grinder", "Brew Group", "Boiler", "Water Tank", and "Drip Tray" with "Tolva", "Molino", "Grupo de infusión", "Caldera", "Depósito de agua", and "Bandeja de goteo"
Constraints: change only the text; preserve layout, typography, spacing, and hierarchy; no extra words; do not alter logos or imagery

identity-preserve

Use case: identity-preserve
Input images: Image 1: person photo; Image 2..N: clothing references
Primary request: replace only the clothing with the provided garments
Constraints: preserve face, body shape, pose, hair, expression, and identity; match lighting and shadows; keep the background unchanged; no accessories or text

precise-object-edit

Use case: precise-object-edit
Input images: Image 1: room photo
Primary request: replace only the white chairs with wooden chairs
Constraints: preserve camera angle, room lighting, floor shadows, and surrounding objects; keep all other aspects unchanged

lighting-weather

Use case: lighting-weather
Input images: Image 1: original photo
Primary request: make it look like a winter evening with gentle snowfall
Constraints: preserve subject identity, geometry, camera angle, and composition; change only lighting, atmosphere, and weather

background-extraction

Use case: background-extraction
Input images: Image 1: product photo
Primary request: isolate the product on a clean transparent background
Scene/backdrop: perfectly flat solid #00ff00 chroma-key background for local background removal
Constraints: background must be one uniform color with no shadows, gradients, texture, reflections, floor plane, or lighting variation; crisp silhouette; generous padding; no halos or fringing; preserve label text exactly; no restyling; do not use #00ff00 anywhere in the subject

Post-process note: after built-in generation, run python "${CODEX_HOME:-$HOME/.codex}/skills/.system/imagegen/scripts/remove_chroma_key.py" --input <source> --out <final.png> --auto-key border --soft-matte --transparent-threshold 12 --opaque-threshold 220 --despill. Ask before using CLI gpt-image-1.5 --background transparent --output-format png for true/native transparency, failed chroma-key validation, or complex subjects such as hair, fur, glass, smoke, liquids, translucent materials, reflections, or soft shadows, unless the user already explicitly requested gpt-image-1.5, scripts/image_gen.py, or CLI fallback.

style-transfer

Use case: style-transfer
Input images: Image 1: style reference
Primary request: apply Image 1's visual style to a man riding a motorcycle on a plain white backdrop
Constraints: preserve palette, texture, and brushwork; no extra elements

compositing

Use case: compositing
Input images: Image 1: base scene; Image 2: subject to insert
Primary request: place the subject from Image 2 next to the person in Image 1
Constraints: match lighting, perspective, and scale; keep the base framing unchanged; no extra elements

character consistency workflow

Use case: identity-preserve
Input images: Image 1: previous character anchor illustration
Primary request: continue the story with the same character in a new scene and action
Scene/backdrop: snowy forest after a winter storm
Subject: same young forest hero gently helping a frightened squirrel out of a fallen tree
Style/medium: same children's book watercolor illustration style as Image 1
Constraints: do not redesign the character; preserve facial features, proportions, outfit, color palette, and personality; no text; no watermark

sketch-to-render

Use case: sketch-to-render
Input images: Image 1: drawing
Primary request: turn the drawing into a photorealistic image
Constraints: preserve layout, proportions, and perspective; choose realistic materials and lighting; do not add new elements or text