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/peterkrueck/claude-code-development-kit/image-gennpx skills add peterkrueck/Claude-Code-Development-Kit --skill image-gengit clone --depth 1 https://github.com/peterkrueck/Claude-Code-Development-KitWhat 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 | $0.00064 | $0.01323 |
| Opus 5 | $0.00032 | $0.00661 |
| Sonnet 5 | $0.00013 | $0.00265 |
| Haiku 4.5 | $0.00006 | $0.00132 |
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 2d 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.
How it starts
The opening of the file, as written. The whole thing — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Image Generation (Gemini)
Generate image variations using Google's Gemini image generation model with reference images for style and character consistency. The model supports up to 14 reference images per request and can maintain consistency across multiple characters.
Prerequisites
- GEMINI_API_KEY environment variable must be set
- Get a key at https://aistudio.google.com/apikey
- The key needs billing enabled for image generation (~$0.067/image at 1K resolution)
- Deno runtime installed (for the generation script)
Workflow
Step 1 — Understand what the user wants
Clarify the subject, pose, expression, context, and where the asset will be used (app screen, social media, website, etc.). This context helps craft the right prompt and choose the right aspect ratio.
Step 2 — Select reference images
Always use 1-2 reference images for consistency:
-
Primary reference (always first): The most canonical image of the character/subject. This anchors identity — face shape, color palette, defining features.
-
Style/pose reference (second, optional): Pick the closest existing approved asset to the target pose. This anchors proportions and art style.
The primary reference anchors identity; the style reference anchors proportions. Both together produce the most consistent results.
Step 3 — Craft the prompt
Write a detailed prompt that describes the exact pose, expression, and style:
- Character/subject description — physical traits that define the character (so the model doesn't drift)
- Pose and expression — what the character is doing
- Style directives — art style, line style, shading approach
- Background — color, scene, or transparent
- Framing — full body, bust, three-quarter view, etc.
Prompt template:
[CHARACTER_DESCRIPTION]. [POSE_AND_EXPRESSION]. [STYLE_DIRECTIVES]. [BACKGROUND]. [VIEW/FRAMING].
Tips:
- Be specific about what each hand/arm is doing — vague descriptions lead to random poses
- Always specify the background explicitly
- Include style keywords consistently (e.g., "flat color fills", "3D render", "watercolor")
What ships with it
1 file 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.
- 2d ago First seen · 127 lines · 64 tokens per session scan A 606239b2406b
image-gen is a skill published in the GitHub repository peterkrueck/Claude-Code-Development-Kit (1,380 stars, last pushed 1mo ago), licensed MIT. It adds 64 tokens to every session and 1,323 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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