Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/psd401/psd-claude-plugins/image-gennpx skills add psd401/psd-claude-plugins --skill image-gengit clone --depth 1 https://github.com/psd401/psd-claude-pluginsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/psd401/psd-claude-plugins/image-gen)<a href="https://agentmods.dev/skills/psd401/psd-claude-plugins/image-gen"><img src="https://agentmods.dev/badge/skills/psd401/psd-claude-plugins/image-gen.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00025 | $0.01241 |
| Opus 5 | $0.00013 | $0.00620 |
| Sonnet 5 | $0.00005 | $0.00248 |
| Haiku 4.5 | $0.00003 | $0.00124 |
Grade A, and why
image-gen scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to image-gen — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Image Generation Skill
Generate professional images, infographics, and diagrams using Google's Nano Banana 2 model (gemini-3.1-flash-image-preview).
Model Capabilities
Nano Banana 2 (released February 26, 2026):
- Text rendering - Accurate, legible text in images
- Google Search grounding - Real-time data (weather, stocks, etc.)
- Subject consistency - Up to 5 characters maintained across generations
- Multi-turn conversation - Iterative refinement
- Up to 14 reference images - For composition and style transfer
- Resolutions: 1K, 2K, 4K
- Aspect ratios: 1:1, 2:3, 3:2, 4:3, 16:9, 21:9
Scripts
All scripts use Python via uv run with inline dependencies.
generate.py - Text to Image
uv run scripts/generate.py "prompt" output.png [aspect_ratio] [size]
Examples:
# Basic image
uv run scripts/generate.py "A cozy coffee shop in autumn" coffee.png
# Infographic with specific aspect ratio
uv run scripts/generate.py "Infographic explaining how neural networks work" nn.png 16:9 2K
# 4K professional image
uv run scripts/generate.py "Professional headshot, studio lighting" headshot.png 3:2 4K
edit.py - Image Editing
uv run scripts/edit.py input.png "edit instructions" output.png
Examples:
# Edit existing image
uv run scripts/edit.py photo.png "Change the background to a beach sunset" edited.png
compose.py - Multi-Image Composition
uv run scripts/compose.py "prompt" output.png --refs image1.png image2.png
Examples:
# Combine styles from multiple images
uv run scripts/compose.py "Combine these styles into a logo" logo.png --refs style1.png style2.png
Workflows
Workflows provide structured approaches for specific visual types. Each workflow follows the PAI 6-step editorial process:
- Extract narrative - Understand the complete story/concept
- Derive visual concept - Single metaphor with 2-3 physical objects
- Apply aesthetic - Define style, colors, mood
- Construct prompt - Build detailed generation instructions
- Generate - Execute via script
- Validate - Check against criteria, regenerate if needed
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 170 lines · 25 tokens per session scan A cf47ecf20cff
image-gen is a skill published in the GitHub repository psd401/psd-claude-plugins (2 stars, last pushed 4d ago), licensed MIT. It adds 25 tokens to every session and 1,241 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to image-gen, differing in 0 lines, and is treated as a copy.
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