A local tool that removes the background from an image and saves it as a transparent PNG. It uses rembg, an image-processing program that runs on your computer without sending the image elsewhere.

Use it on character art, mascots, photos, or other images that need a transparent background, with optional trimming or a custom output path.

It removes the manual work of cutting out subjects from photos or artwork and can trim the leftover transparent space.

bg-remove — Skill by peterkrueck

Install

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.

agentmods

npx agentmods add skills/peterkrueck/claude-code-development-kit/bg-remove

Any agent

npx skills add peterkrueck/Claude-Code-Development-Kit --skill bg-remove

Clone the repo

git clone --depth 1 https://github.com/peterkrueck/Claude-Code-Development-Kit

Made for: Claude Code, Codex.

Per session 36 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,372 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. Scan, not verified.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00036	$0.01372
Opus 5	$0.00018	$0.00686
Sonnet 5	$0.00007	$0.00274
Haiku 4.5	$0.00004	$0.00137

Measured 3d ago against content hash 605e5686e673, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 3d 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.

skills/bg-remove/SKILL.md · 140 lines

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:

Read the full file on GitHub · 140 lines

Changes

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.

3d ago First seen · 140 lines · 36 tokens per session scan A 605e5686e673

Subscribe to this mod's changes

bg-remove is a skill published in the GitHub repository peterkrueck/Claude-Code-Development-Kit (1,380 stars, last pushed 1mo ago), licensed MIT. It adds 36 tokens to every session and 1,372 once invoked, about $0.0002 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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bg-remove