Borrowing it
Nothing to install: this file belongs to holocode-ai/rembg-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/holocode-ai/rembg-mcp/main/CLAUDE.mdgit clone --depth 1 https://github.com/holocode-ai/rembg-mcpWrote 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/instructions/holocode-ai/rembg-mcp/claude-md)<a href="https://agentmods.dev/instructions/holocode-ai/rembg-mcp/claude-md"><img src="https://agentmods.dev/badge/instructions/holocode-ai/rembg-mcp/claude-md/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/instructions/holocode-ai/rembg-mcp/claude-md"><img src="https://agentmods.dev/badge/instructions/holocode-ai/rembg-mcp/claude-md.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 | $0.01157 | $0.01157 |
| Opus 5 | $0.00579 | $0.00579 |
| Sonnet 5 | $0.00231 | $0.00231 |
| Haiku 4.5 | $0.00116 | $0.00116 |
Grade A, and why
rembg-mcp CLAUDE.md 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 9d 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 — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
Project Overview
This is an MCP (Model Context Protocol) implementation for rembg, a popular Python library for removing image backgrounds using machine learning models. The main rembg codebase is located in a separate directory and this directory serves as the MCP wrapper implementation.
Rembg is a tool that removes image backgrounds using various pre-trained models including U2Net, BiRefNet, ISNet, and SAM. It supports both CLI usage and library integration with multiple input/output formats (PIL, OpenCV, bytes).
Development Commands
Python Environment
- Python Version: 3.10-3.13 (as specified in rembg's setup.py)
- Package Manager: pip with setuptools
Testing
# Run tests (if rembg source is available)
python -m pytest
Development Installation
# Install rembg in development mode
pip install -e ".[dev,cpu,cli]" # or [gpu] for GPU support
Code Quality
# Code quality tools
black . # Code formatting
flake8 . # Linting
isort . # Import sorting
mypy . # Type checking
bandit . # Security analysis
Architecture Overview
Core Components (from github/rembg/)
- rembg/bg.py - Main background removal logic with
remove()function - rembg/session_factory.py - Factory for creating model sessions (
new_session()) - rembg/sessions/ - Model-specific implementations:
base.py- BaseSession abstract class- Individual model sessions (u2net, birefnet, isnet, sam, etc.)
- rembg/commands/ - CLI command implementations (i, p, s, b subcommands)
- rembg/cli.py - Main CLI entry point using Click
Session Pattern
The library uses a session pattern for model management:
new_session(model_name)creates a session with ONNX runtime- Sessions handle model downloading, normalization, and prediction
- Reusing sessions across multiple images improves performance
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.
- 9d ago First seen · 144 lines · 1,157 tokens per session scan A 5fe5568640ab
rembg-mcp CLAUDE.md is an instructions file published in the GitHub repository holocode-ai/rembg-mcp (4 stars, last pushed 11mo ago), licensed MIT. It adds 1,157 tokens to every session, about $0.0058 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 2026-08-31.
Other instructions, from other repositories
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.