AI Image Generator

Generate images using AI APIs (Google Gemini and OpenAI GPT). This skill teaches the prompting patterns and API mechanics for producing professional images directly from Claude Code.

Managed alternative

If you don't want to manage API keys, ImageBot provides a managed image generation service with album templates and brand kit support. Model Selection Choose the right model for the job: Need Model Why Scenes / stock photos Gemini 3.1 Flash Image Best depth, complexity, environmental context Transparent icons / logos GPT Image 1.5 Native RGBA alpha channel ( background: "transparent" ) Text on images GPT Image 1.5 90% accurate text rendering Drafts / iteration Gemini 2.5 Flash Image Free tier (~500/day) Final client assets Gemini 3 Pro Image Higher detail, better style consistency Model IDs Model API ID Provider Gemini 3.1 Flash Image gemini-3.1-flash-image-preview Google AI Gemini 3 Pro Image gemini-3-pro-image-preview Google AI Gemini 2.5 Flash Image gemini-2.5-flash-image Google AI GPT Image 1.5 gpt-image-1.5 OpenAI Verify model IDs before use — they change frequently: curl -s "https://generativelanguage.googleapis.com/v1beta/models?key= $GEMINI_API_KEY " | python3 -c "import sys,json; [print(m['name']) for m in json.load(sys.stdin)['models'] if 'image' in m['name'].lower()]" The 5-Part Prompting Framework Build prompts in this order for consistent results: 1. Image Type Set the genre: "A photorealistic photograph", "An isometric illustration", "A flat vector icon" 2. Subject Who or what, with specific details: "of a warm, approachable Australian woman in her early 30s, smiling naturally" 3. Environment Setting and spatial relationships: "in a bright modern home with terracotta decor on wooden shelves behind her" 4. Technical Specs Camera and lighting: "Shot at 85mm f/2.0, natural window light, head and shoulders framing" 5. Constraints What to exclude: "Photorealistic, no text, no watermarks, no logos" Example (Good vs Bad) BAD — keyword soup: "professional woman, spa, warm lighting, high quality, 4K" GOOD — narrative direction: "A professional skin treatment scene in a warm clinical setting. A practitioner wearing blue medical gloves uses a microneedling pen on the client's forehead. The client lies on a white treatment bed, eyes closed, relaxed. Warm golden-hour light from a window to the left. Terracotta-toned wall visible in the background. Shot at 85mm f/2.0, shallow depth of field. No text, no watermarks." Workflow 1. Determine Image Need Purpose Aspect Ratio Model Hero banner 16:9 or 21:9 Gemini Service card 4:3 or 3:4 Gemini Profile / avatar 1:1 Gemini Icon / badge 1:1 GPT (transparent) OG / social share 1.91:1 Gemini Instagram post 1:1 or 4:5 Gemini Mobile hero 9:16 Gemini 2. Build the Prompt Use the 5-part framework. Refer to references/prompting-guide.md for detailed photography parameters. 3. Generate via API Gemini (Python — handles shell escaping correctly) python3 << 'PYEOF' import json , base64 , urllib . request , os , sys GEMINI_API_KEY = os . environ . get ( "GEMINI_API_KEY" ) if not GEMINI_API_KEY : print ( "Set GEMINI_API_KEY environment variable" ) ; sys . exit ( 1 ) model = "gemini-3.1-flash-image-preview" url = f"https://generativelanguage.googleapis.com/v1beta/models/ { model } :generateContent?key= { GEMINI_API_KEY } " prompt = """A professional photograph of a modern co-working space in Newcastle, Australia. Natural light floods through floor-to-ceiling windows. Three people collaborate at a standing desk — one pointing at a laptop screen. Exposed brick wall, potted fiddle-leaf fig, coffee cups on the desk. Shot at 35mm f/4.0, environmental portrait style. No text, no watermarks, no logos.""" payload = json . dumps ( { "contents" : [ { "parts" : [ { "text" : prompt } ] } ] , "generationConfig" : { "responseModalities" : [ "TEXT" , "IMAGE" ] , "temperature" : 0.8 } } ) . encode ( ) req = urllib . request . Request ( url , data = payload , headers = { "Content-Type" : "application/json" , "User-Agent" : "ImageGen/1.0" } ) resp = urllib . request . urlopen ( req , timeout = 120 ) result = json . loads ( resp . read ( ) )

Extract image from response

for part in result [ "candidates" ] [ 0 ] [ "content" ] [ "parts" ] : if "inlineData" in part : img_data = base64 . b64decode ( part [ "inlineData" ] [ "data" ] ) output_path = "hero-image.png" with open ( output_path , "wb" ) as f : f . write ( img_data ) print ( f"Saved: { output_path } ( { len ( img_data ) : , } bytes)" ) break PYEOF GPT (Transparent Icons) python3 << 'PYEOF' import json , base64 , urllib . request , os , sys OPENAI_API_KEY = os . environ . get ( "OPENAI_API_KEY" ) if not OPENAI_API_KEY : print ( "Set OPENAI_API_KEY environment variable" ) ; sys . exit ( 1 ) url = "https://api.openai.com/v1/images/generations" payload = json . dumps ( { "model" : "gpt-image-1.5" , "prompt" : "A minimal, clean plumbing wrench icon. Flat design, single consistent stroke weight, modern style. On a transparent background." , "n" : 1 , "size" : "1024x1024" , "background" : "transparent" , "output_format" : "png" } ) . encode ( ) req = urllib . request . Request ( url , data = payload , headers = { "Content-Type" : "application/json" , "Authorization" : f"Bearer { OPENAI_API_KEY } " } ) resp = urllib . request . urlopen ( req , timeout = 120 ) result = json . loads ( resp . read ( ) ) img_data = base64 . b64decode ( result [ "data" ] [ 0 ] [ "b64_json" ] ) with open ( "icon-wrench.png" , "wb" ) as f : f . write ( img_data ) print ( f"Saved: icon-wrench.png ( { len ( img_data ) : , } bytes)" ) PYEOF 4. Save and Optimise Save generated images to .jez/artifacts/ or the user's specified path. Post-processing (optional):

Convert to WebP for web use

python3 -c " from PIL import Image img = Image.open('hero-image.png') img.save('hero-image.webp', 'WEBP', quality=85) print(f'WebP: {img.size[0]}x{img.size[1]}') "

Trim whitespace from transparent icons

python3 -c " from PIL import Image img = Image.open('icon.png') trimmed = img.crop(img.getbbox()) trimmed.save('icon-trimmed.png') " 5. Quality Check (Optional) Send the generated image back to a vision model for QA:

Send to Gemini Flash for critique

critique_prompt

"""Review this image for: 1. AI artifacts (extra fingers, floating objects, text errors) 2. Technical accuracy (wrong equipment, unsafe positioning) 3. Composition issues (awkward cropping, cluttered background) 4. Style consistency with a professional stock photo List any issues found, or say 'PASS' if the image is production-ready.""" If issues are found, append them as negative guidance to the original prompt and regenerate. Multi-Turn Editing Gemini supports editing a generated image across conversation turns. The key requirement: preserve thought signatures from model responses.

Turn 1: Generate base image

contents

[ { "role" : "user" , "parts" : [ { "text" : "Scene prompt..." } ] } ]

The response includes thoughtSignature on parts — preserve them ALL

Turn 2: Edit the image

contents

[ { "role" : "user" , "parts" : [ { "text" : "Original prompt" } ] } , { "role" : "model" , "parts" : response_parts_with_signatures } ,

Keep intact

{

"role"

:

"user"

,

"parts"

:

[

{

"text"

:

"Edit: change the wall colour to blue. Keep everything else exactly the same."

}

]

}

]

Edit prompt pattern

Always specify what to KEEP unchanged, not just what to change. The model treats unlisted elements as free to modify. GOOD: "Edit this image: keep the people, desk, and window unchanged. Only change: wall colour from terracotta to ocean blue." BAD: "Now make the wall blue." (Model may change everything else too) API Key Setup Provider Get key at Env variable Google Gemini aistudio.google.com GEMINI_API_KEY OpenAI platform.openai.com OPENAI_API_KEY export GEMINI_API_KEY = "your-key-here" export OPENAI_API_KEY = "your-key-here" Common Mistakes Mistake Fix Using curl for Gemini prompts Use Python — shell escaping breaks on apostrophes "Beautiful, professional, high quality" Use concrete specs: "85mm f/1.8, golden hour light" Not specifying what to exclude Always end with "No text, no watermarks, no logos" Requesting transparent PNG from Gemini Gemini cannot do transparency — use GPT with background: "transparent" American defaults for AU businesses Explicitly specify "Australian" + local architecture, vegetation Generic data for model ID Verify current model IDs — they change frequently