--- name: sandbox-execution-guide-2141a1ebfe36 description: "Secure sandboxed code execution environments for reproducible research computing" metadata: openclaw: emoji: "🛡️" category: "tools" subcategory: "code-exec" keywords: ["sandbox execution", "code runner", "literate programming", "containerization", "reproducible computing"] source: "wentor" --- # Sandbox Execution Guide A skill for setting up and using sandboxed code execution environments for research computing. Covers containerized execution, security considerations, resource management, and integration with research workflows. ## Why Sandboxed Execution? Research code often requires: - Isolation from the host system for security - Reproducible environments across machines - Resource limits to prevent runaway computations - Multi-language support (Python, R, Julia, MATLAB) ## Docker-Based Sandboxes ### Creating a Research Container ```dockerfile # Dockerfile for a reproducible research environment FROM python:3.11-slim # System dependencies RUN apt-get update && apt-get install -y --no-install-recommends \ build-essential \ gfortran \ libopenblas-dev \ && rm -rf /var/lib/apt/lists/* # Create non-root user for security RUN useradd -m -s /bin/bash researcher USER researcher WORKDIR /home/researcher # Pin all dependencies COPY requirements.txt . RUN pip install --user --no-cache-dir -r requirements.txt # Copy project files COPY --chown=researcher:researcher . /home/researcher/project WORKDIR /home/researcher/project # Resource limits set at runtime, not build time CMD ["python", "main.py"] ``` ### Running with Resource Limits ```bash # Run with CPU, memory, and time constraints docker run \ --cpus="2.0" \ --memory="4g" \ --memory-swap="4g" \ --pids-limit=100 \ --network=none \ --read-only \ --tmpfs /tmp:size=512m \ --timeout 3600 \ research-sandbox:latest python analysis.py # Mount data as read-only, output directory as writable docker run \ -v /data/raw:/data:ro \ -v /data/results:/output:rw \ --cpus="4.0" \ --memory="16g" \ research-sandbox:latest python pipeline.py ``` ## Python Sandbox with Resource Limits ### Process-Level Isolation ```python import subprocess import resource import signal import tempfile import os def run_sandboxed(code: str, timeout: int = 60, max_memory_mb: int = 512) -> dict: """ Execute Python code in a sandboxed subprocess with resource limits. Args: code: Python code string to execute timeout: Maximum execution time in seconds max_memory_mb: Maximum memory in megabytes """ with tempfile.NamedTemporaryFile(mode='w', suffix='.py', delete=False) as f: f.write(code) script_path = f.name try: result = subprocess.run( ['python', '-u', script_path], capture_output=True, text=True, timeout=timeout, env={ 'PATH': '/usr/bin:/usr/local/bin', 'HOME': '/tmp', 'PYTHONDONTWRITEBYTECODE': '1' } ) return { 'stdout': result.stdout, 'stderr': result.stderr, 'returncode': result.returncode, 'timed_out': False } except subprocess.TimeoutExpired: return { 'stdout': '', 'stderr': f'Execution timed out after {timeout}s', 'returncode': -1, 'timed_out': True } finally: os.unlink(script_path) # Example usage result = run_sandboxed(""" import numpy as np data = np.random.randn(1000) print(f"Mean: {data.mean():.4f}") print(f"Std: {data.std():.4f}") """, timeout=30, max_memory_mb=256) print(result['stdout']) ``` ## Nix-Based Reproducible Environments For maximum reproducibility, use Nix to pin every dependency including system libraries: ```nix # shell.nix for a research project { pkgs ? import (fetchTarball { url = "https://github.com/NixOS/nixpkgs/archive/nixos-23.11.tar.gz"; }) {} }: pkgs.mkShell { buildInputs = with pkgs; [ python311 python311Packages.numpy python311Packages.scipy python311Packages.pandas python311Packages.matplotlib python311Packages.scikit-learn R rPackages.ggplot2 rPackages.dplyr ]; shellHook = '' echo "Research sandbox activated" echo "Python: $(python --version)" echo "R: $(R --version | head -1)" ''; } ``` ```bash # Enter the reproducible environment nix-shell shell.nix # Or use flakes for even better reproducibility nix develop ``` ## Security Best Practices When running untrusted or third-party code: 1. **Network isolation**: Use `--network=none` in Docker to prevent data exfiltration 2. **Filesystem restrictions**: Mount data as read-only, limit writable paths 3. **Resource caps**: Always set CPU, memory, and time limits 4. **User isolation**: Run as non-root user inside the container 5. **Syscall filtering**: Use seccomp profiles to restrict system calls 6. **Output sanitization**: Validate and sanitize all output before processing ## Integration with CI/CD Automate research pipeline execution with GitHub Actions: ```yaml name: Research Pipeline on: push: paths: ['src/**', 'data/**'] jobs: run-analysis: runs-on: ubuntu-latest container: image: research-sandbox:latest options: --cpus 4 --memory 8g steps: - uses: actions/checkout@v4 - run: python src/01_preprocess.py - run: python src/02_analyze.py - run: python src/03_visualize.py - uses: actions/upload-artifact@v4 with: name: results path: output/ ``` This ensures every commit triggers a fresh, sandboxed execution of the full pipeline, catching environment-dependent bugs and ensuring reproducibility. --- Source attribution: wentorai Original project: https://github.com/wentorai/research-plugins/blob/bf44b3cd617fa94c8a1b254c5d1987142ca3d631/skills/tools/code-exec/sandbox-execution-guide/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Wentor AI Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: wentorai Original project: https://github.com/wentorai/research-plugins/blob/bf44b3cd617fa94c8a1b254c5d1987142ca3d631/skills/tools/code-exec/sandbox-execution-guide/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering, Education & Research Roles: Backend Developer, Data Scientist, DevOps / SRE, ML / AI Engineer, Researcher Specialties: Backend, DevOps Tools: Docker, Nix Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 227a48d5a4588e5c51168a50147090e543c99e18bf16d4ffefd9af4107fabe12