yulinlina

shell-sieve

Community yulinlina
Updated

An MCP server and CLI wrapper that executes shell commands and intelligently compresses, filters, and extracts errors from verbose output to save AI agent context windows.

shell-sieve

An MCP server and CLI wrapper that executes shell commands and intelligently compresses, filters, and extracts errors from verbose output to save AI agent context windows.

License Language Status PyPI version License: MIT MCP Server

๐ŸŽฏ Why?

AI coding agents frequently exhaust their context windows when running verbose commands like npm install or pytest, leading to hallucinations or dropped errors. Existing tools either blindly truncate output (losing the stack trace) or dump everything; Shell Sieve intelligently strips ANSI, collapses repetitive progress lines, and guarantees error extraction.

Target audience: AI agent developers, users of Claude Code/Cursor/Cline, and CLI power users who want clean, actionable command output without the noise.

โœจ Features

  • โœจ ANSI stripping and smart line collapsing (e.g., progress bars)
  • โœจ Error-aware truncation (always keeps the last N lines and any detected stack traces/errors)
  • โœจ Native MCP Server implementation for seamless integration with Cursor, Claude Desktop, and Cline

๐Ÿš€ Quick Start

# Install
pip install shell-sieve

# Run
shell-sieve --help

๐Ÿ“ฆ Installation

From Source

git clone https://github.com/YOUR_USERNAME/shell-sieve.git
cd shell-sieve
# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate

# Install in development mode
pip install -e ".[dev]"

# Run tests
pytest -v

๐ŸŽฌ Demo

The GIF above was recorded using Charm VHS:

vhs < demo.tape

๐Ÿ“– Usage

# Show help
shell-sieve --help

# Common usage examples
shell-sieve --example

๐Ÿ—๏ธ Architecture

graph LR
    A[Input] --> B[Core Engine]
    B --> C[Output]
    B --> D[Plugins]
    D --> E[Extensions]

๐Ÿค Contributing

Contributions are welcome! Please:

  1. Fork the repo
  2. Create a feature branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'Add amazing feature')
  4. Push to the branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

๐Ÿ“„ License

MIT ยฉ 2026 โ€” See LICENSE for details.

If this project helped you, please โญ star it!

Made with โค๏ธ and AI

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