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-editgit 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-edit)<a href="https://agentmods.dev/skills/peterkrueck/claude-code-development-kit/image-edit"><img src="https://agentmods.dev/badge/skills/peterkrueck/claude-code-development-kit/image-edit/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-edit"><img src="https://agentmods.dev/badge/skills/peterkrueck/claude-code-development-kit/image-edit.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 | $0.00095 | $0.01140 |
| Opus 5.5 | $0.00038 | $0.00456 |
| Sonnet 5.5 | $0.00019 | $0.00228 |
| Haiku 4.5 | $0.00010 | $0.00114 |
Grade A, and why
image-edit 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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Image Edit — Crop, Resize & Transform
Precision image manipulation using Python/Pillow. This skill exists because macOS sips has unreliable crop offset behavior and visual inspection alone leads to bad coordinates — images often have hundreds of pixels of invisible padding that throws off naive crops.
Setup
The scripts need Pillow and numpy. Create a temp venv on first use:
python3 -m venv /tmp/imgcrop && /tmp/imgcrop/bin/pip install Pillow numpy -q
This only needs to happen once per session. The venv at /tmp/imgcrop persists until reboot.
The Golden Rule: Measure Before You Cut
Never guess crop coordinates from visual inspection. Images routinely have large invisible regions — transparent padding, solid-color borders, or dead space — that make visual estimates wildly wrong.
Always run the analysis script first to get exact pixel coordinates of where the actual content lives.
Workflow
Step 1 — Visual inspection
Use the Read tool to look at the image. Understand what's in it and what the user wants to focus on.
Step 2 — Analyze content bounds
Run the bundled analysis script to find where content actually lives:
/tmp/imgcrop/bin/python3 .claude/skills/image-edit/scripts/analyze_bounds.py <image_path>
This outputs JSON with:
content_bounds— exact pixel coordinates of non-background contentpadding— how much dead space exists on each sidesuggested_square_crops— pre-calculated crop regions at different zoom levels:tight_head(35%) — face/head closeupupper_body(55%) — head through chest/armsthree_quarter(75%) — head through waistfull(100%) — entire subject
Use --threshold to adjust sensitivity (default 30).
Step 3 — Calculate crop coordinates
Use the analysis output to compute exact crop coordinates:
- Headroom: Add 40-70px above the content top
- Centering: Center horizontally on the content's center-x, not the image's center
- Aspect ratio: For square crops, use
max(width, height)as the side length - Clamping: Ensure the crop region doesn't extend beyond image dimensions
What ships with it
2 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.
- 1mo ago First seen · 117 lines · 95 tokens per session scan A 99c8df81adfa
image-edit is a skill published in the GitHub repository peterkrueck/Claude-Code-Development-Kit (1,388 stars, last pushed 3mo ago), licensed MIT. It adds 95 tokens to every session and 1,140 once invoked, about $0.0004 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
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Optimize the meaningful UI available immediately from a Next.js route on an initial load (hard navigation) or named client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route and entry point at a time; the shipped test then guards…