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 michaelboeding/skills --skill background-removegit clone --depth 1 https://github.com/michaelboeding/skillsWrote 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/michaelboeding/skills/background-remove)<a href="https://agentmods.dev/skills/michaelboeding/skills/background-remove"><img src="https://agentmods.dev/badge/skills/michaelboeding/skills/background-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/michaelboeding/skills/background-remove"><img src="https://agentmods.dev/badge/skills/michaelboeding/skills/background-remove.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00057 | $0.01444 |
| Opus 5.5 | $0.00023 | $0.00578 |
| Sonnet 5.5 | $0.00011 | $0.00289 |
| Haiku 4.5 | $0.00006 | $0.00144 |
Grade A, and why
background-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 2mo 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 — 207 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Background Remove Skill
Remove backgrounds from images using AI (rembg/U2-Net) or built-in methods.
Output: PNG or WebP with transparent background.
Quick Examples
| User Says | What Happens |
|---|---|
| "Remove the background from this photo" | AI removes background, outputs PNG |
| "Make this image transparent" | Removes background, preserves subject |
| "Cut out the product from this image" | Isolates subject with clean edges |
| "Remove backgrounds from all images in /photos" | Batch processes multiple images |
| "Quick background removal, white background" | Uses fast built-in method |
Prerequisites
-
rembg- AI-based background removal (recommended)pip install rembg # Or with GPU acceleration (faster, requires CUDA) pip install rembg[gpu] -
Pillow- Required for image processingpip install Pillow
The first run will download the U2-Net model (~170MB) which is cached for future use.
Methods
| Method | Description | Best For |
|---|---|---|
| rembg | AI-based using U2-Net model | Complex images, photos, products (default) |
| builtin | White-to-transparent conversion | Icons, graphics with clean white backgrounds |
Workflow
Step 1: Gather Requirements (REQUIRED)
Use the AskUserQuestion tool for each question. Ask ONE question at a time.
Q1: Image Source
"Which image(s) should I remove the background from?
Please provide the file path or paste the image."
Wait for response.
Q2: Method (Optional)
"Which removal method?
- AI (rembg) - Best quality, works on any image (default)
- Built-in - Faster, best for white backgrounds"
Wait for response. Default to AI if user doesn't specify.
Q3: Output Location (Optional)
"Where should I save the result?
- Same location with
_nobgsuffix (default)- Custom path"
Wait for response.
Step 2: Execute Background Removal
Single image:
python3 ${SKILL_PATH}/skills/background-remove/scripts/background_remove.py \
-i "/path/to/image.jpg" \
-o "/path/to/output.png"
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.
- 2mo ago First seen · 207 lines · 57 tokens per session scan A 418541707d73
background-remove is a skill published in the GitHub repository michaelboeding/skills (30 stars, last pushed 27d ago), licensed MIT. It adds 57 tokens to every session and 1,444 once invoked, about $0.0002 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.
background-remove compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| background-removemichaelboeding/skills | Skill | 30 | 27d ago | MIT | A | 57 | Claude Code |
| imagegenopenai/codex | Skill | 134k | today | Apache-2.0 | A | 113 | Codex |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 2d ago | MIT | A | 200 | Claude Code, Codex |
| playwright-climicrosoft/playwright | Skill | 100k | today | Apache-2.0 | A | 19 | Claude Code |
| browser-automationopenclaw/openclaw | Skill | 394k | today | MIT | A | 36 | Claude Code |
| systematic-debuggingobra/superpowers | Skill | 315k | 6d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |