Claude Code Development Kit is a starter workflow that gives Claude Code project context, working notes, verification commands, review commands, and hooks for maintaining them across sessions. It is for beginners and intermediate users building applications or side projects who want Claude Code to understand the codebase and check its work. The catalogue entries are the kit's skills and commands.
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 skills add peterkrueck/Claude-Code-Development-Kit --skill image-gengit clone --depth 1 https://github.com/peterkrueck/Claude-Code-Development-KitWrote 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/peterkrueck/claude-code-development-kit/image-gen)<a href="https://agentmods.dev/skills/peterkrueck/claude-code-development-kit/image-gen"><img src="https://agentmods.dev/badge/skills/peterkrueck/claude-code-development-kit/image-gen/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/peterkrueck/claude-code-development-kit/image-gen"><img src="https://agentmods.dev/badge/skills/peterkrueck/claude-code-development-kit/image-gen.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00064 | $0.01323 |
| Opus 5.5 | $0.00026 | $0.00529 |
| Sonnet 5.5 | $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 1mo 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.
- 1mo 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 (1,388 stars, last pushed 3mo ago), licensed MIT. It adds 64 tokens to every session and 1,323 once invoked, about $0.0003 per session on Opus 5.5. 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.
Other skills, from other repositories
playtest-report
Structured playtest report template, or turn existing playtest notes into structured feedback.
godot-signals-groups
Build event-driven, decoupled Godot 4.7 gameplay with signals and node groups: declare and emit custom signals, connect with Callables (incl. bind/one-shot), and broadcast to many nodes via groups and callgroup. Use when wiring node communication in a Godot project, replacing tight references with signals…
motion
Reconstruct a skeleton from calibrated mesh projections and sequential VLM joint annotations, then fit joints and skin weights. Generate motion from joint-position trajectories, rhythm and contact constraints through IK; refine bone orientation against the action, then export, retarget and validate on the target mesh.
unity-addressables
Manage Addressables groups, entries, profiles and content builds (com.unity.addressables, reflection-based).
unity-manual-component
Manually add, configure, reorder, and copy components on GameObjects using Unity Editor UI. For one-off Inspector workflows that do not need REST automation.
threejs-exposure-color-grading
Build a measured exposure and grading path in Three.js. Use for a 64x36 encoded luminance meter, asynchronous readback, weighted log-average exposure, asymmetric adaptation, single tone-map ownership, and a generated 32-cube post-tone-map LUT.