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 bg-removegit 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/bg-remove)<a href="https://agentmods.dev/skills/peterkrueck/claude-code-development-kit/bg-remove"><img src="https://agentmods.dev/badge/skills/peterkrueck/claude-code-development-kit/bg-remove/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/bg-remove"><img src="https://agentmods.dev/badge/skills/peterkrueck/claude-code-development-kit/bg-remove.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 | $0.00036 | $0.01372 |
| Opus 5.5 | $0.00014 | $0.00549 |
| Sonnet 5.5 | $0.00007 | $0.00274 |
| Haiku 4.5 | $0.00004 | $0.00137 |
Grade A, and why
bg-remove 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Background Remove — Local AI Background Removal
Remove backgrounds from images using rembg (local, offline, no data sent externally). Outputs RGBA PNG with proper transparency.
Input
Arguments after /bg-remove:
- Source image path (required) — path to the image
--trim(optional) — auto-trim transparent padding after removal--output <path>(optional) — custom output path. Default: same directory,<name>-transparent.png
Examples:
/bg-remove assets/character/mascot.png/bg-remove image.png --trim/bg-remove image.png --output ~/Desktop/result.png
Setup
rembg is installed in a dedicated venv. Always activate it before use:
source ~/.claude/tools/rembg-env/bin/activate
If the venv doesn't exist, install it:
python3 -m venv ~/.claude/tools/rembg-env && source ~/.claude/tools/rembg-env/bin/activate && pip install "rembg[cpu,cli]"
Model files are cached in ~/.u2net/ (downloaded on first use per model, ~170MB for birefnet-general).
Process
Step 1: Verify Input
- Check the source image exists
- Get dimensions:
sips -g pixelWidth -g pixelHeight <path> - View the image with the Read tool to understand what we're working with
Step 2: Remove Background
Use the birefnet-general model — validated in testing on illustrated/character art and general photos, producing clean edges across both.
source ~/.claude/tools/rembg-env/bin/activate && rembg i -m birefnet-general <input> <output>
Model choice: Default to birefnet-general. In side-by-side testing it gave clean edges on both illustrated subjects and photographic ones. Avoid anime-trained models (e.g. isnet-anime): on non-anime and even some illustrated inputs they tend to add artifacts and leave dark patches around edges. If birefnet-general underperforms on a specific image, compare against another general model rather than an anime-specific one.
Step 3: Verify Result
The Read tool renders transparency as black, so you MUST verify by compositing on a colored background:
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 · 140 lines · 36 tokens per session scan A 605e5686e673
bg-remove is a skill published in the GitHub repository peterkrueck/Claude-Code-Development-Kit (1,388 stars, last pushed 3mo ago), licensed MIT. It adds 36 tokens to every session and 1,372 once invoked, about $0.0001 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.
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