image-gen

name	image-gen
version	2.1.0
description	Image generation, editing, and review via OpenRouter API. Five models from budget to premium. Style presets for series consistency, JSON structured prompts, reference image anchoring, system message support, prompt upsampling. Vision-based quality review loop. Zero dependencies beyond Python stdlib. Use when: generate, create, draw, design, illustrate, edit, or modify images.
tools_required	["python3 (stdlib only, no pip deps)"]
triggers	["generate image, create image, draw, design, illustrate, sketch, render","make me a picture, create a logo, design a poster, generate artwork","edit image, modify image, change the background, add text to image","style transfer, make it look like, convert to watercolor","inpainting, remove object, replace background","user sends image + modification request"]

image-gen

Image generation and editing via OpenRouter. Five models, three scripts, style presets, one JSON contract.

Scripts: ./scripts/generate.py, ./scripts/edit.py, ./scripts/review.py Presets: ./presets/*.json Output dir: ./data/

Setup

export OPENROUTER_API_KEY_IMAGES='your-api-key-here'

• Claude Code: copy this skill folder into .claude/skills/image-gen/
• Codex CLI: append this SKILL.md content to your project's root AGENTS.md

For the full installation walkthrough (prerequisites, API keys, verification, troubleshooting), see references/installation-guide.md.

Credential management

Three tiers for managing the OPENROUTER_API_KEY_IMAGES environment variable:

1. Vault skill (recommended): If you have a vault or secret-management skill, store the key there and export it before running scripts. Example: export OPENROUTER_API_KEY_IMAGES=$(vault get OPENROUTER_API_KEY_IMAGES)
2. Custom secret manager: Use your team's preferred secret manager (1Password CLI, AWS Secrets Manager, etc.)
3. Plain export: export OPENROUTER_API_KEY_IMAGES='your-api-key-here' in your shell profile

Optional keys for additional features:

• OPENAI_API_KEY -- for mask-based inpainting via edit.py --mode openai
• ANTHROPIC_API_KEY -- for auto-review via review.py --auto

---

Model Selection

What do you need?
  |
  +-- Fast + cheap + good enough?
  |     --> nanobanana (~$0.0004/image)
  |
  +-- High quality, no text?
  |     --> flux.2-pro (best visual quality)
  |
  +-- Text in the image?
  |     --> gpt-5-image (best text rendering)
  |
  +-- Image editing?
  |     +-- Describe changes in words --> gpt-5-image or nanobanana-pro
  |     +-- Paint mask area to change --> edit.py --mode openai
  |
  +-- Budget generation at scale?
  |     --> flux.2-klein (fastest, cheapest Flux)
  |
  +-- Quality + editing + reasoning?
        --> nanobanana-pro (best balance)

Alias	Type	Cost	Best For
flux.2-pro	Image-only	~$0.03/MP	Default high-quality generation
flux.2-klein	Image-only	~$0.014/MP	Fast, budget generation
gpt-5-image	Text+Image	~$0.04/image	Text rendering, complex edits
nanobanana-pro	Text+Image	~$0.012/image	Balanced quality + editing
nanobanana	Text+Image	~$0.0004/image	Lowest-cost generation

Full comparison: ./references/model-card.md

---

Quick Reference

# Generate with default model (flux.2-pro)
python ./scripts/generate.py \
  --prompt "A red fox in snow" \
  --output-dir ./data/

# Generate with style preset
python ./scripts/generate.py \
  --prompt "A scene description for consistent series" \
  --preset default \
  --output-dir ./data/

# Generate with style reference image
python ./scripts/generate.py \
  --prompt "A new scene" \
  --style-ref /path/to/golden-image.png \
  --output-dir ./data/

# Generate with multiple style refs
python ./scripts/generate.py \
  --prompt "Scene desc" \
  --style-ref /path/to/ref1.png \
  --style-ref /path/to/ref2.png

# Generate with system message (GPT-5 / NanoBanana only)
python ./scripts/generate.py \
  --prompt "Scene desc" \
  --model gpt-5-image \
  --system-prompt "You generate muted watercolor illustrations..."

# Generate with prompt upsampling disabled
python ./scripts/generate.py \
  --prompt "Exact scene" \
  --model flux.2-pro \
  --no-prompt-upsampling

# Generate with options (model, aspect ratio, size)
python ./scripts/generate.py \
  --prompt "Tokyo skyline at sunset" \
  --model nanobanana-pro \
  --aspect-ratio 16:9 \
  --size 2K \
  --output-dir ./data/

# Generate with text (GPT-5 Image)
python ./scripts/generate.py \
  --prompt 'Poster with text "HELLO WORLD" in bold sans-serif typography' \
  --model gpt-5-image \
  --output-dir ./data/

# Edit image (chat-based)
python ./scripts/edit.py \
  --mode openrouter \
  --input-image ./data/input.png \
  --prompt "Change the background to a sunset beach" \
  --model gpt-5-image \
  --output-dir ./data/

# Edit image (mask-based)
python ./scripts/edit.py \
  --mode openai \
  --input-image ./data/input.png \
  --mask ./data/mask.png \
  --prompt "Replace masked area with a small bonsai tree" \
  --openai-size 1024x1024 \
  --output-dir ./data/

# Review quality (auto mode)
python ./scripts/review.py \
  --image ./data/output.png \
  --original-prompt "A red fox in snow" \
  --auto

---

Style Presets

Presets encode visual identity into reusable JSON files. A preset defines palette, composition, rendering style, model defaults, and system messages.

How Presets Work

1. Pick a preset based on the project context
2. generate.py --preset <name> loads the preset JSON
3. The script applies the preset: enhances the prompt with style data, selects model defaults, injects system messages
4. For Flux models: prompt is constructed as JSON (structured prompt, prevents concept bleeding)
5. For GPT-5/NanoBanana: style block is prepended as natural language, system message is injected

Available Presets

Preset	File	Description
default	presets/default.json	No style constraints. Quality-focused defaults.

Preset Schema

{
  "name": "preset-name",
  "description": "What this preset is for",
  "defaults": {
    "model": "flux.2-pro",
    "aspect_ratio": "3:2",
    "size": "2K"
  },
  "style": {
    "description": "Overall style description",
    "color_palette": ["#hex1", "#hex2", "#hex3"],
    "mood": "Emotional tone",
    "lighting": "Lighting description",
    "composition": "Composition rules",
    "rendering": "Rendering constraints",
    "camera": {"angle": "...", "framing": "..."},
    "anti_patterns": ["thing to avoid", "another thing"],
    "reference_images": ["/absolute/path/to/golden.png"]
  },
  "system_message": "System prompt for GPT-5/NanoBanana models"
}

Priority Order (CLI > Preset > Hardcoded)

• --model flag overrides preset.defaults.model
• --aspect-ratio flag overrides preset.defaults.aspect_ratio
• --size flag overrides preset.defaults.size
• --system-prompt flag overrides preset.system_message
• If no preset and no flag: hardcoded defaults (flux.2-pro, 1:1, 2K)

Creating a New Preset

1. Copy presets/default.json as a template
2. Set name and description
3. Fill defaults with preferred model, aspect ratio, size
4. Fill style with palette (HEX values), composition rules, rendering constraints
5. Write system_message for GPT-5/NanoBanana (ignored by Flux)
6. Optionally add reference_images paths for visual anchoring
7. Test: python generate.py --prompt "test scene" --preset your-preset

---

Style References

Use --style-ref to pass reference images for visual anchoring. The script prepends a style transfer instruction automatically.

# Single reference (anchor to a "golden" image)
python ./scripts/generate.py --prompt "New scene" \
  --style-ref /path/to/golden.png

# Multiple references (combine style + content refs, up to 8)
python ./scripts/generate.py --prompt "New scene" \
  --style-ref /path/to/style-ref.png \
  --style-ref /path/to/character-ref.png

Reference images from presets (style.reference_images) are automatically loaded alongside CLI refs.

Full workflow and per-model consistency techniques: ./references/style-consistency.md

---

System Messages

Use --system-prompt to set persistent style context for GPT-5 Image and NanoBanana models. System messages are injected as the system role, keeping the user prompt focused on scene content only.

python ./scripts/generate.py \
  --prompt "A quiet village at dawn" \
  --model gpt-5-image \
  --system-prompt "You generate muted watercolor illustrations with earth-tone palettes..."

System messages can also be set in presets via the system_message field. CLI --system-prompt overrides preset system messages.

Flux models do not support system messages (the flag is silently ignored).

---

Prompt Upsampling

Flux models support prompt_upsampling -- an API feature that auto-enhances basic prompts into richer descriptions before generation.

• Default: ON for Flux models (flux.2-pro, flux.2-klein)
• Disable: --no-prompt-upsampling when you want exact prompt control
• Enable: --prompt-upsampling (explicit, same as default)
• Non-Flux models: Flag is silently ignored

# Default: upsampling ON (good for short/simple prompts)
python ./scripts/generate.py --prompt "A cat" --model flux.2-pro

# Disable: exact prompt control (good for precise/pre-enhanced prompts)
python ./scripts/generate.py --prompt "Detailed exact scene..." \
  --model flux.2-pro --no-prompt-upsampling

---

Prompt Engineering

Enhance-before-generate protocol

1. Identify intent: photo, illustration, logo, diagram, etc.
2. Select preset if this is series work.
3. Fill missing details: subject, style, lighting, composition, camera/look.
4. Tune prompt shape to model: concise for Flux, more explicit for GPT-5/NanoBanana family.
5. Add quality boosters that match requested style.

Example: raw prompt -> enhanced prompt

• Raw prompt: a cat in space
• Enhanced prompt: Orange tabby cat in a custom spacesuit floating in zero gravity, Earth visible in helmet reflection, dramatic rim lighting, cinematic framing, ultra-detailed, high contrast, clean background

Templates, per-model formats, JSON structured prompts, and examples: ./references/prompt-templates.md

---

Generation Workflow

1. Select Preset (if applicable)

If the image is part of a series or project with established visual identity, select the appropriate preset.

2. Enhance Prompt

Transform the raw prompt into a model-optimized prompt. Each model interprets prompts differently:

• Flux: Concise, front-loaded, under 80 words. JSON for complex multi-element scenes. HEX colors for precision.
• GPT-5 Image: Detailed natural language. System messages for series work. Text in quotes for rendering.
• NanoBanana: Structured narrative. Constrain palette explicitly for muted styles ("no neon, desaturated").

3. Generate

python ./scripts/generate.py \
  --prompt "[enhanced prompt]" \
  --model [chosen model] \
  --preset [preset if applicable] \
  --aspect-ratio [ratio] \
  --size [1K|2K|4K]

Output: JSON to stdout with path, model, cost_estimate, generation_time_ms.

When a preset is used, the script handles prompt enhancement internally. Enhance the scene description but do NOT manually add style parameters -- the preset handles that.

4. Review (Optional)

For quality-critical work:

python ./scripts/review.py \
  --image /path/to/output.png \
  --original-prompt "[the enhanced prompt]" \
  --auto

Output: JSON with score, verdict (accept/refine/reject), critique, suggested_refinement.

• accept (score >= 7): Deliver to user
• refine (score 4-6): Re-generate with suggested_refinement applied
• reject (score < 4): Re-generate with different model or reworked prompt

5. Deliver

Return the file path and a brief description.

---

Editing Workflow

Chat-Based Editing (OpenRouter)

For natural language edits: "make it darker", "change background to beach", "add a hat".

python ./scripts/edit.py --mode openrouter \
  --input-image /path/to/original.png \
  --prompt "Change the background to a sunset beach" \
  --model nanobanana-pro

Best models for chat editing: gpt-5-image, nanobanana-pro.

Mask-Based Inpainting (OpenAI Direct)

For precise area editing with a mask image.

python ./scripts/edit.py --mode openai \
  --input-image /path/to/original.png \
  --mask /path/to/mask.png \
  --prompt "A fluffy orange cat" \
  --openai-size 1024x1024

Requires OPENAI_API_KEY. Mask: PNG with transparent areas where edits should happen.

---

Script Output Contract

All scripts output JSON to stdout. Parse JSON, never text.

generate.py success

{
  "success": true,
  "path": "/abs/path/to/20260217-143052-flux-2-pro.png",
  "all_paths": ["/abs/path/to/20260217-143052-flux-2-pro.png"],
  "model": "black-forest-labs/flux.2-pro",
  "prompt": "the user prompt",
  "enhanced_prompt": "the preset-enhanced prompt (if different)",
  "preset": "my-preset",
  "aspect_ratio": "16:9",
  "size": "2K",
  "cost_estimate": "~$0.120",
  "generation_time_ms": 8500,
  "image_count": 1,
  "style_refs": ["/path/to/ref.png"],
  "system_message_used": true
}

generate.py / edit.py error

{
  "success": false,
  "error": "HTTP 429: Too Many Requests",
  "details": "Rate limit exceeded...",
  "model": "black-forest-labs/flux.2-pro",
  "generation_time_ms": 150
}

review.py output (auto mode)

{
  "success": true,
  "mode": "auto",
  "score": 8,
  "prompt_adherence": 9,
  "technical_quality": 8,
  "composition": 7,
  "verdict": "accept",
  "critique": "Strong prompt adherence with vivid colors. Minor composition issue with empty right third.",
  "suggested_refinement": ""
}

---

CLI Flags Reference

generate.py

Flag	Default	Description
--prompt	required	Generation prompt
--model	preset or flux.2-pro	Model alias or full OpenRouter ID
--aspect-ratio	preset or 1:1	1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9
--size	preset or 2K	1K, 2K, 4K
--output-dir	./data/	Output directory
--output-file	auto-named	Explicit output path
--seed	none	Random seed (model-dependent)
--input-image	none	Input image for editing
--preset	none	Style preset name or path to JSON file
--style-ref	none	Style reference image path (repeatable, up to 8)
--prompt-upsampling	on for Flux	Enable Flux prompt_upsampling
--no-prompt-upsampling		Disable prompt_upsampling
--system-prompt	preset or none	System message for GPT-5/NanoBanana

edit.py

Flag	Default	Description
--mode	required	openrouter or openai
--input-image	required	Image to edit
--prompt	required	Edit instruction
--mask	none	Mask PNG (openai mode)
--model	gpt-5-image	Model for openrouter mode
--openai-model	gpt-image-1	Model for openai mode
--openai-size	none	Size for openai mode
--openai-quality	none	Quality for openai mode
--output-dir	./data/	Output directory
--output-file	auto-named	Explicit output path

review.py

Flag	Default	Description
--image	required	Image to review
--original-prompt	required	Prompt used for generation
--auto	false	Auto-review via Anthropic API

---

Cost Tracking

Report cost after every generation. Use the cost_estimate field from script output.

Model	Typical Cost
NanoBanana	$0.0004
Flux 2 Klein	$0.01-0.03
NanoBanana Pro	$0.01-0.03
Flux 2 Pro	$0.03-0.12
GPT-5 Image	$0.03-0.10

Tip: for batch generation (5+ images), prefer NanoBanana or Flux 2 Klein to control spend.

---

Error Handling

Error	Action
No API key	Script exits with JSON error. Check env vars.
HTTP 429 (rate limit)	Wait 10s, retry. If persistent, switch model.
HTTP 402 (no credits)	Top up OpenRouter account.
No images in response	Check model supports image output. Try different model.
Timeout (>180s)	Model may be overloaded. Try Klein or NanoBanana for speed.
Image quality too low	Run review loop. Refine prompt or switch to higher-quality model.
Preset not found	Check presets/ directory. Use preset name without .json extension.

---

Anti-Patterns

Do NOT	Do Instead
Send raw user prompts to models	Always enhance prompts first
Use Flux for text in images	Use GPT-5 Image or NanoBanana Pro
Use GPT-5 for bulk generation	Use NanoBanana or Flux Klein (10-100x cheaper)
Skip cost reporting	Always report estimated cost
Retry same prompt on failure	Rework prompt or switch model
Use review loop for casual requests	Reserve for quality-critical work
Forget to set API key before running	Export required keys before running scripts
Use JSON prompts for GPT-5 Image	GPT-5 prefers natural language; JSON adds no benefit
Use verbal color descriptions with Flux	Use HEX values in palette for precise control
Generate series images without a preset	Create a preset for any 3+ image series
Forget --style-ref for consistency	Use golden image as reference for series work

---

Bundled Resources Index

Path	What	When to Load
./scripts/generate.py	Core image generation script	Every generation task
./scripts/edit.py	Chat-based and mask-based editing	Image modification requests
./scripts/review.py	Vision-based quality review	Quality-critical workflows
./presets/default.json	Default preset (no style constraints)	Reference for preset schema
./references/prompt-templates.md	SOTA prompt engineering: per-model formats, JSON templates, style modifiers, enhancement protocol	Prompt engineering step
./references/style-consistency.md	Reference image workflow, seed workflow, consistency technique stack	Generating image series requiring visual consistency
./references/model-card.md	Model capabilities, tradeoffs, pricing context	Model selection and optimization
./references/installation-guide.md	Detailed install walkthrough for Claude Code and Codex CLI	First-time setup or environment repair
./references/api-reference.md	API payload and integration details	Debugging and advanced usage
./references/book-to-prompts-playbook.md	Method for extracting visual prompts from literature	Book illustration projects
./examples/	Example outputs by model	Visual quality and style calibration
./UPDATES.md	Changelog for this skill	Checking new features/fixes
./UPDATE-GUIDE.md	Agent-oriented update instructions	Applying updates safely

---

Staying Updated

This skill ships with an UPDATES.md changelog and UPDATE-GUIDE.md for your AI agent.

After installing, tell your agent: "Check UPDATES.md in the image-gen skill for any new features or changes."

When updating, tell your agent: "Read UPDATE-GUIDE.md and apply the latest changes from UPDATES.md."

Follow UPDATE-GUIDE.md so customized local files are diffed before any overwrite.

To check upstream updates directly from GitHub:

curl -fsSL https://raw.githubusercontent.com/buildoak/fieldwork-skills/main/skills/image-gen/UPDATES.md | head -40