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imggen
Use this skill when users want to generate images using OpenAI's image generation API (DALL-E or gpt-image-1), or extract text from images using OCR. Invoke when users request AI-generated images, artwork, logos, illustrations, visual content from text prompts, or need to extract text/data from i...
Full skill instructions
Generate images from text prompts and extract text from images using OpenAI's APIs.
imggen is a command-line tool that interfaces with OpenAI's image generation API. It supports multiple models (gpt-image-1, dall-e-3, dall-e-2) and provides options for image size, quality, format, and style.
imggen binary installed and available in PATHOPENAI_API_KEY environment variable set with a valid OpenAI API keyimggen [flags] "prompt"
| Flag | Short | Default | Description |
|---|---|---|---|
--model | -m | gpt-image-1 | Model: gpt-image-1, dall-e-3, dall-e-2 |
--size | -s | 1024x1024 | Image dimensions |
--quality | -q | auto | Quality level |
--count | -n | 1 | Number of images (1-10 for gpt-image-1, 1 for dall-e-3) |
--output | -o | auto-generated | Output filename or directory |
--format | -f | png | Output format: png, jpeg, webp |
--style | vivid | Style for dall-e-3: vivid, natural | |
--transparent | -t | false | Transparent background (gpt-image-1 + png/webp only) |
--prompt | -P | Prompt (can be specified multiple times) | |
--parallel | -p | 1 | Number of parallel workers for multiple prompts |
--api-key | $OPENAI_API_KEY | Override API key |
OPENAI_API_KEY is set in the environmentThe tool outputs:
Generated files are saved to the current working directory with timestamp-based names (e.g., image-20251216-120000.png) unless --output is specified.
All image generation costs are automatically logged to ~/.imggen/sessions.db. View costs using the cost subcommand:
# View total costs
imggen cost
# View today's costs
imggen cost today
# View this week's costs (last 7 days)
imggen cost week
# View this month's costs (last 30 days)
imggen cost month
# View costs by provider
imggen cost provider
In interactive mode (imggen -i), use the cost or $ command:
cost today - Today's costscost week - This week's costscost month - This month's costscost total - All-time totalcost provider - Breakdown by providercost session - Current session's costsManage the SQLite database storing sessions and cost data:
# Reset database (delete all data)
imggen db reset
# Reset with backup of old data
imggen db reset --backup
# Show database location and stats
imggen db info
imggen "a sunset over mountains"
imggen -m dall-e-3 -s 1792x1024 -q hd "panoramic view of a futuristic city"
imggen -n 4 -q high "abstract geometric pattern"
imggen -t -f png "minimalist tech company logo, flat design"
imggen -o hero-image.png "website hero banner with gradient"
imggen -m dall-e-3 --style natural "professional headshot, studio lighting"
# Generate multiple images with --prompt flag
imggen --prompt "a sunset" --prompt "a cat" --prompt "a dog" -o ./output
# Short form with parallel processing (3 workers)
imggen -P "sunset" -P "mountains" -P "ocean" -o ./images -p 3
# From a text file (one prompt per line)
imggen batch prompts.txt -o ./output
# From a JSON file with per-prompt options
imggen batch prompts.json -o ./output
# With parallel processing
imggen batch prompts.txt -o ./output -p 3
Generate multiple images from command-line prompts using the --prompt/-P flag:
imggen --prompt "a sunset over mountains" --prompt "a cat playing piano" -o ./output
This processes all prompts and saves images to the output directory with indexed filenames:
001-a-sunset-over-mountains.png002-a-cat-playing-piano.pngUse --parallel/-p to control concurrent processing (default: 1 = sequential).
Generate multiple images from a file of prompts using the batch subcommand:
imggen batch <input-file> [flags]
Text file (.txt) - One prompt per line (lines starting with # are ignored):
a sunset over mountains
a cat playing piano
abstract geometric art
JSON file (.json) - Array of objects with optional per-prompt settings:
[
{"prompt": "a sunset over mountains"},
{"prompt": "a cat playing piano", "model": "dall-e-3", "quality": "hd"},
{"prompt": "abstract art", "size": "1792x1024"}
]
| Flag | Short | Default | Description |
|---|---|---|---|
--output | -o | current dir | Output directory |
--model | -m | gpt-image-1 | Default model |
--size | -s | model default | Default image size |
--quality | -q | model default | Default quality level |
--format | -f | png | Output format |
--parallel | -p | 1 | Number of parallel workers |
--stop-on-error | false | Stop on first error | |
--delay | 0 | Delay between requests (ms) |
Common errors and solutions:
OPENAI_API_KEY environment variable--count valueCosts per image (USD):
| Size | Low | Medium | High |
|---|---|---|---|
| 1024x1024 | $0.011 | $0.042 | $0.167 |
| 1536x1024 | $0.016 | $0.063 | $0.250 |
| 1024x1536 | $0.016 | $0.063 | $0.250 |
| Size | Standard | HD |
|---|---|---|
| 1024x1024 | $0.040 | $0.080 |
| 1024x1792 | $0.080 | $0.120 |
| 1792x1024 | $0.080 | $0.120 |
| Size | Cost |
|---|---|
| 256x256 | $0.016 |
| 512x512 | $0.018 |
| 1024x1024 | $0.020 |
Extract text from images using OpenAI's vision API with optional structured output support.
imggen ocr <image-path> [flags]
| Flag | Short | Default | Description |
|---|---|---|---|
--model | -m | gpt-5-mini | Model: gpt-5.2, gpt-5-mini, gpt-5-nano |
--schema | -s | JSON schema file for structured output | |
--schema-name | extracted_data | Name for the JSON schema | |
--suggest-schema | false | Suggest a JSON schema based on image content | |
--prompt | -p | auto | Custom extraction prompt |
--output | -o | stdout | Output file |
--url | Image URL instead of file path | ||
--api-key | $OPENAI_API_KEY | Override API key | |
--verbose | -v | false | Log HTTP requests and responses |
| Model | Cost (Input) | Cost (Output) | Best For |
|---|---|---|---|
| gpt-5-nano | $0.05/1M tokens | $0.40/1M tokens | Ultra budget, simple text |
| gpt-5-mini | $0.25/1M tokens | $2.00/1M tokens | Cost-effective, most OCR tasks |
| gpt-5.2 | $1.75/1M tokens | $14.00/1M tokens | Complex documents, highest accuracy |
imggen ocr document.png
imggen ocr --url https://example.com/image.png
imggen ocr receipt.jpg -o extracted.txt
# Create a schema file (invoice_schema.json):
# {
# "type": "object",
# "properties": {
# "vendor": {"type": "string"},
# "date": {"type": "string"},
# "total": {"type": "number"},
# "items": {
# "type": "array",
# "items": {
# "type": "object",
# "properties": {
# "name": {"type": "string"},
# "price": {"type": "number"}
# },
# "required": ["name", "price"],
# "additionalProperties": false
# }
# }
# },
# "required": ["vendor", "date", "total"],
# "additionalProperties": false
# }
imggen ocr receipt.jpg --schema invoice_schema.json -o invoice.json
# Analyze image and suggest appropriate schema
imggen ocr document.png --suggest-schema
# Save suggested schema to file
imggen ocr document.png --suggest-schema -o suggested_schema.json
imggen ocr complex-document.pdf -m gpt-5.2
imggen ocr business-card.jpg -p "Extract the name, title, email, and phone number"
When using the --schema flag, the output will be structured JSON matching your schema. This is useful for:
The schema must follow JSON Schema draft-07 format with additionalProperties: false for strict validation.
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