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Gpt image skill

Skill feiskyer/claude-code-settings/skills/gpt-image-skill

Generate or edit images using OpenAI GPT Image API (gpt-image-2, gpt-image-1, etc). Use ONLY when the user explicitly names OpenAI or GPT as the provider: "gpt image", "openai image", "generate image with openai", "用 openai 画图", "用 GPT 生成图片". For generic image requests without a provider, use nanobanana-skill instead. Do NOT use for diagrams (架构图/流程图) — draw those with Mermaid or code.From its SKILL.md

Install
npx -y skills add feiskyer/claude-code-settings --skill gpt-image-skill

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • reads credentialsReads from 3 credential sources: `~/.gpt-image.env` and 2 more.
  • runs commandsInstructs the agent to run 3 commands, including `python3 -m pip install -r ${CLAUDE_SKILL_DIR}/requirements.txt` and 2 more.

SKILL.md

5.4 KB, ~1.3k tokens by cl100k_base, as published. Nobody here has run it

GPT Image Skill

Generate or edit images using OpenAI's GPT Image models through a bundled Python script.

Requirements

  1. OPENAI_API_KEY: Must be configured in ~/.gpt-image.env or export OPENAI_API_KEY=<your-key>
  2. OPENAI_API_BASE (optional): Custom API base URL for compatible endpoints (e.g. Azure OpenAI, proxies). Set in ~/.gpt-image.env or export it.
  3. Python3 with dependencies: openai, Pillow. Install via python3 -m pip install -r ${CLAUDE_SKILL_DIR}/requirements.txt if not installed yet.
  4. Executable: ${CLAUDE_SKILL_DIR}/gpt_image.py

Instructions

For image generation

  1. Ask the user for:

    • What they want to create (the prompt)
    • Desired size (optional, defaults to 1024x1024)
    • Output filename (optional, auto-generates UUID-based name if not specified)
    • Model preference (optional, defaults to gpt-image-2)
    • Quality (optional, defaults to auto)
    • Number of images (optional, defaults to 1)
  2. Run the script:

    python3 ${CLAUDE_SKILL_DIR}/gpt_image.py --prompt "description of image" --output "filename.png"
    
  3. Show the user the saved image path when complete.

For image editing

  1. Ask the user for:

    • Input image file(s) to edit (up to 3)
    • What changes they want (the prompt)
    • Output filename (optional)
  2. Run with input images:

    python3 ${CLAUDE_SKILL_DIR}/gpt_image.py edit --prompt "editing instructions" --input image1.png image2.png --output "edited.png"
    

Available Options

Models (--model)

  • gpt-image-2 (default) — Latest model with strong instruction following, text rendering, and broad world knowledge
  • gpt-image-1.5 — Mid-tier model
  • gpt-image-1 — First-generation GPT image model
  • gpt-image-1-mini — Lightweight, faster generation

Sizes (--size)

  • 1024x1024 (default) — Square
  • 1024x1536 — Portrait (2:3)
  • 1536x1024 — Landscape (3:2)
  • auto — Let the model decide

Quality (--quality)

  • auto (default) — Model decides optimal quality
  • high — Higher detail, slower
  • medium — Balanced
  • low — Fastest

Output Format (--format)

  • png (default) — Lossless
  • jpeg — Smaller file size
  • webp — Modern format, good compression

Background (--background)

  • auto (default) — Model decides
  • transparent — Transparent background (png/webp only)
  • opaque — Solid background

Other Options

  • --n <count> — Number of images to generate (default: 1)
  • --output <filename> — Output filename (default: auto-generated)

Examples

Generate a simple image

python3 ${CLAUDE_SKILL_DIR}/gpt_image.py --prompt "A serene mountain landscape at sunset with a lake"

Generate with specific size and output

python3 ${CLAUDE_SKILL_DIR}/gpt_image.py \
  --prompt "Modern minimalist logo for a tech startup" \
  --size 1024x1024 \
  --quality high \
  --output "logo.png"

Generate landscape image

python3 ${CLAUDE_SKILL_DIR}/gpt_image.py \
  --prompt "Futuristic cityscape with flying cars" \
  --size 1536x1024 \
  --output "cityscape.png"

Generate with transparent background

python3 ${CLAUDE_SKILL_DIR}/gpt_image.py \
  --prompt "A cute cartoon cat mascot" \
  --background transparent \
  --format png \
  --output "mascot.png"

Generate multiple images

python3 ${CLAUDE_SKILL_DIR}/gpt_image.py \
  --prompt "Abstract art in the style of Kandinsky" \
  --n 3 \
  --output "art.png"

Edit existing images

python3 ${CLAUDE_SKILL_DIR}/gpt_image.py edit \
  --prompt "Add a rainbow in the sky" \
  --input photo.png \
  --output "photo-with-rainbow.png"

Combine multiple reference images

python3 ${CLAUDE_SKILL_DIR}/gpt_image.py edit \
  --prompt "Create a gift basket containing all items shown" \
  --input item1.png item2.png item3.png \
  --output "gift-basket.png"

Use a different model

python3 ${CLAUDE_SKILL_DIR}/gpt_image.py \
  --prompt "Detailed portrait of a cat in watercolor style" \
  --model gpt-image-1 \
  --output "cat-portrait.png"

Error Handling

If the script fails:

  • Check that OPENAI_API_KEY is exported
  • If using a custom endpoint, verify OPENAI_API_BASE is correct
  • Verify input image files exist and are readable (for editing)
  • Ensure the output directory is writable
  • Check that the model name is valid

Best Practices

  1. Be descriptive in prompts — include style, mood, colors, composition details
  2. For logos/icons, use square size (1024x1024) with transparent background
  3. For social media, use portrait (1024x1536) for stories or square for posts
  4. For wallpapers/headers, use landscape (1536x1024)
  5. Use high quality for final output, auto for quick iterations
  6. GPT Image models excel at text rendering — include text in prompts when needed
  7. For editing, provide clear instructions about what to change and what to keep

What ships with it: 3 files

6.8 KB alongside SKILL.md, 1 of them executable

evals/

Gives 0 of the 12 instructions most context ai engineering skills give in ~1.3k tokens

Counted across 1,328 of the 2,349 authors here whose files we hold, read 2026-09-06

  • Dispatch a fresh subagent for each taskin 76 of 1328, across 59 files
  • Perform spec compliance review before code quality reviewin 44 of 1328, across 34 files
  • Dispatch a final code reviewer after all tasksin 38 of 1328, across 26 files
  • Answer subagent questions before allowing implementationin 36 of 1328, across 26 files
  • Use the least powerful model capable of the taskin 33 of 1328, across 26 files
  • Create a TodoWrite list for all tasksin 32 of 1328, across 22 files
  • Perform a task review after each implementationin 31 of 1328, across 24 files
  • Extract all tasks and context from the planin 29 of 1328, across 20 files
  • Provide full task text to subagentsin 28 of 1328, across 20 files
  • Use git worktrees for isolated workspacesin 25 of 1328, across 20 files
  • Specify the model explicitly when dispatching a subagentin 23 of 1328, across 18 files
  • Execute all tasks from the plan without stoppingin 21 of 1328, across 16 files

Said here and by no other author read

  • Ask user for prompt and image details
  • Run the python script with specified arguments
  • Show the user the saved image path

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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