---

name: gpt-image description: This skill should be used when the user asks to "generate an image", "edit this image", "make a hero banner", "create a product mockup", "remove the background", "make bitmap variations", or needs help creating raster visuals with a CLI-first GPT Image workflow plus an explicit Codex path when requested.

GPT Image Skill

Generate or edit raster images from Claude Code, GitHub Copilot CLI, or Codex-style environments while preserving a consistent two-mode workflow.

Goal

• Make one image skill work in Claude and Copilot without assuming those hosts can call an image tool directly.
• Default to the bundled GPT Image CLI workflow for ordinary generation and editing.
• Preserve an explicit Codex-backed path for users who specifically want Codex/native generation.
• Keep raster image work separate from vector or code-native asset work.

Two top-level modes

This skill has exactly two top-level modes:

• Explicit CLI mode (default): use scripts/image_gen.py for normal generation and editing requests unless the user explicitly asks for Codex/native generation. This path requires network access and OPENAI_API_KEY.
• Codex mode (explicit opt-in): in Claude Code and Copilot-style runtimes, invoke the local Codex CLI (codex exec) only when the user explicitly asks for Codex, native generation, or a Codex-built-in image workflow. This path does not require OPENAI_API_KEY, but it does require a working codex installation and auth.

If this skill is used from Codex itself, Codex mode maps to Codex's built-in image workflow directly. If this skill is used from Claude or Copilot, Codex mode means "delegate to Codex CLI" rather than "call a host-native image tool directly."

Within the default CLI mode, the bundled CLI exposes three subcommands:

• generate
• edit
• generate-batch

Rules:

• In Claude Code and Copilot-style runtimes, do not claim that a direct host-native image tool exists unless the runtime actually exposes one.
• Default to the bundled CLI mode for normal image generation and editing.
• Use Codex mode only when the user explicitly asks for Codex/native generation.
• Before running either mode, ask for any missing image parameters that materially affect the result or export.
• If the user explicitly says to use defaults, you may proceed with the documented CLI defaults instead of asking one-by-one.
• If CLI mode is unavailable because OPENAI_API_KEY is missing, tell the user how to set it locally and mention that Codex mode exists only as an explicit opt-in path.
• Never modify scripts/image_gen.py unless the user explicitly asks to change the bundled CLI.

Codex mode in Claude/Copilot

When this skill runs in Claude Code or GitHub Copilot CLI, Codex mode means shelling out to Codex CLI only after explicit user opt-in.

Use these rules:

1. Use codex exec for non-interactive runs.
2. Pass -C <workspace> so Codex works in the current project.
3. If the current directory is not a Git repository, add --skip-git-repo-check.
4. For local input images, attach them with repeated -i flags and label each image's role in the prompt.
5. Ask Codex to save project-bound finals into the workspace before finishing, usually under output/gpt-image/.
6. Do not use CLI-only controls such as quality, input_fidelity, masks, background, output format, or direct API parameters in Codex mode.

Recommended Codex command shapes:

Generate:

codex exec -C "$PWD" --skip-git-repo-check \
  "Generate a blog header image for a post about AI agents. Save the selected final to output/gpt-image/blog-header.png and report the final path. Use Codex's built-in image workflow, not scripts/image_gen.py."

Edit:

codex exec -C "$PWD" --skip-git-repo-check -i input.png \
  "Image 1 is the edit target. Replace only the background with a soft blue gradient. Keep the subject unchanged. Save the final to output/gpt-image/input-edited.png and report the final path. Use Codex's built-in image workflow, not scripts/image_gen.py."

Codex output policy:

• Codex may initially place generated images under $CODEX_HOME/generated_images/....
• If the image is meant for the current project, move or copy the selected final into the workspace before finishing.
• Never leave a project-referenced asset only under $CODEX_HOME/generated_images/....
• Do not overwrite an existing asset unless the user explicitly asked for replacement; otherwise create a versioned sibling such as hero-v2.png.

When to use

• Generate new raster images such as hero art, blog headers, product shots, game assets, textures, photoreal scenes, or bitmap infographics.
• Edit existing raster images such as background extraction, object replacement, text replacement, lighting changes, compositing, or style transfer.
• Produce multiple image variants when the end result should still be a bitmap asset.

When not to use

• Extending or matching an existing repo-native SVG, icon, logo, or illustration system.
• Simple diagrams, wireframes, icons, or production vector/logo assets that are better produced directly in SVG, HTML/CSS, canvas, or draw.io.
• Small edits to assets that already exist in an editable native format and should stay there.
• Any task where the user clearly wants deterministic code-native output instead of generated bitmap output.

Decision points

Think about three separate questions:

1. Mode: default CLI mode or explicit Codex mode?
2. Intent: generate a new image or edit an existing one?
3. Execution strategy: one asset, a few targeted variants, or many jobs (generate-batch only in explicit CLI mode)?

Interpret inputs conservatively:

• If the user wants to preserve an existing image while changing part of it, treat the request as an edit.
• If provided images are only style, composition, or mood references, treat the request as generate with references.
• Label every image by role: edit target, style reference, composition reference, or insert/compositing input.

Default workflow

1. Confirm the task should produce a raster image rather than a vector or code-native asset.
2. Choose the top-level mode. Stay in explicit CLI mode unless the user explicitly chooses Codex, native generation, or Codex CLI.
3. Choose generate vs edit.
4. Collect prompt, exact text, constraints, avoid list, all input images, and execution parameters before running.
5. Use references/prompting.md for shared prompt structure and iteration rules. Use references/sample-prompts.md as recipes, not as mandatory augmentation.
6. In default CLI mode, use the bundled scripts/image_gen.py workflow and the CLI-only docs in references/cli.md and references/image-api.md.
7. In Codex mode from Claude/Copilot, use codex exec and instruct Codex to save the selected final into the workspace when the asset is project-bound.
8. Save project-bound finals into the workspace. Do not leave a project asset only in a tool-owned scratch location.
9. Do not overwrite an existing asset unless the user explicitly asked for replacement. Otherwise create a versioned sibling such as hero-v2.png.
10. Report the final saved path, the final prompt, and whether CLI mode or Codex mode was used.

Ask for missing parameters

Do not silently choose user-facing image parameters when they are missing.

For generate, ask for any missing items that materially affect output:

• model
• size or aspect ratio
• quality
• whether the image should include text
• exact text, if text is required
• output format or transparency/background behavior when relevant
• style/use case if the request is too vague to execute reliably

For edit, ask for any missing items that materially affect output:

• model
• which image is the edit target and the role of any additional images
• exact edit scope (change only X)
• invariants (keep Y unchanged)
• quality
• input_fidelity
• whether a mask is required and the mask path if applicable
• output format or transparency/background behavior when relevant

If the user already supplied a parameter, do not re-ask it. If the user says "use defaults", you may use the CLI defaults documented in references/cli.md.

Prompt shaping

Use short labeled specs when the request is non-trivial. Favor normalization over invention.

Recommended labels:

Use case: <taxonomy slug>
Asset type: <where the image will be used>
Primary request: <main instruction>
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>

Taxonomy slugs:

• Generate: photorealistic-natural, product-mockup, ui-mockup, infographic-diagram, logo-brand, illustration-story, stylized-concept, historical-scene
• Edit: text-localization, identity-preserve, precise-object-edit, lighting-weather, background-extraction, style-transfer, compositing, sketch-to-render

CLI conventions

These conventions apply in the default CLI mode:

• Preferred package manager: uv
• Final outputs: output/gpt-image/
• Temporary JSONL or scratch files: tmp/gpt-image/
• OPENAI_API_KEY must be set locally
• Do not ask the user to paste the full API key into chat

Install dependency if needed:

uv pip install openai
pip install openai

Optional downscaling support:

uv pip install pillow
pip install pillow

Resources

• references/prompting.md - shared prompt structure, specificity, and iteration rules
• references/sample-prompts.md - copy/paste prompt recipes shared by both modes
• references/cli.md - default CLI usage for scripts/image_gen.py
• references/image-api.md - GPT Image API and CLI-only parameter reference
• scripts/image_gen.py - bundled CLI implementation