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