Gpt Image SkillCAUTION

Curated skills, sub-agents, and config templates that supercharge Claude Code — research, image gen, GitHub automation & more.

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: gpt-image-skill
description: '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.'
allowed-tools: Read, Write, Glob, Grep, Task, Bash(cat:*), Bash(ls:*), Bash(tree:*), Bash(python3:*)
---

# 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:

   ```bash
   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:

   ```bash
   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

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

### Generate with specific size and output

```bash
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

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

### Generate with transparent background

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

### Generate multiple images

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

### Edit existing images

```bash
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

```bash
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

```bash
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

CAUTIONgrade B · trust 89/100 Install with care. The audit found things worth knowing before you trust its output.

What the source does

Filesystem

none-observed

Network

declared (1 observation(s))

Shell

none-observed

Dependencies

not all pinned

Secrets in source

none-found

Findings (5)

plugins/codex-skill/skills/codex-skill

plugins/nanobanana-skill/skills/nanobanana-skill

plugins/youtube-transcribe-skill/skills/youtube-transcribe-skill

img_bytes = base64.b64decode(image_data.b64_json)

python-dotenv, openai, Pillow, httpx

Gates applied: no_behavioural_pass.