genpark-agent-sandboxed-virtual-code-runner-skill
Health Warn
- License — License: NOASSERTION
- Description — Repository has a description
- Active repo — Last push 0 days ago
- Low visibility — Only 7 GitHub stars
Code Fail
- exec() — Shell command execution in client.py
- rm -rf — Recursive force deletion command in client.py
Permissions Pass
- Permissions — No dangerous permissions requested
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Deterministic sandboxed virtual code execution engine and AST security analyzer verifying multi-language agent code, blocking dangerous syscalls, and capturing isolated execution telemetry.
genpark-agent-sandboxed-virtual-code-runner-skill
Production-Grade AI Agent Infrastructure Skill • 100% Standard Library Python • Native Model Context Protocol (MCP)
📌 Overview & Capability
genpark-agent-sandboxed-virtual-code-runner-skill is a deterministic, high-performance, zero-dependency Python tool and native Model Context Protocol (MCP) server designed for autonomous AI agents, multi-agent frameworks (LangGraph, CrewAI, AutoGen, OpenAI Swarm), and developer environments (Cursor, Windsurf, Claude Desktop).
Executive Capability: Deterministic sandboxed virtual code execution engine and AST security analyzer verifying multi-language agent code, blocking dangerous syscalls, and capturing isolated execution telemetry.
⚡ Key Highlights
- 🐍 Zero External
pipDependencies: Implemented entirely with pure Python standard library for instant zero-overhead execution. - 🔌 Native Model Context Protocol (MCP): Plugs directly into any MCP-compliant client via JSON-RPC 2.0 stdio.
- ⚡ Sub-Millisecond Execution: Slashes token burn and latency by resolving routine agent tasks deterministically without frontier LLM round-trips.
- 🛡️ Production-Hardened: Comprehensive error handling, boundary validation, and telemetry.
🏗️ Architecture
graph LR
Agent([🤖 Autonomous Agent / IDE]) -->|MCP Protocol / JSON-RPC| Server[⚡ genpark-agent-sandboxed-virtual-code-runner-skill Server]
Server --> Core[🧠 Deterministic Processing Core]
Core --> Out[📊 Actionable Result & Telemetry]
Out --> Agent
🚀 Quickstart & Usage
1. Direct Python Client Execution
python example_usage.py
2. Programmatic Integration
from client import AgentSandboxedCodeRunner
client = AgentSandboxedCodeRunner()
result = client.run_sandbox_benchmark()
print(result)
🔌 Model Context Protocol (MCP) Setup
Connect this skill to Claude Desktop, Cursor, or any MCP-compliant client:
claude_desktop_config.json
{
"mcpServers": {
"genpark-agent-sandboxed-virtual-code-runner-skill": {
"command": "python",
"args": ["/path/to/genpark-agent-sandboxed-virtual-code-runner-skill/mcp_server.py"]
}
}
}
Direct MCP Testing
python mcp_server.py --test
📊 Technical Specifications
| Parameter | Type | Required | Description |
|---|---|---|---|
payload |
string / dict |
Yes | Primary context, code, schema, or content input |
options |
dict |
No | Execution flags, compression ratios, or risk bounds |
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