monty-skill

name	monty-skill
description	Run AI-generated Python in a hard sandbox (Pydantic Monty) with enforced time + memory limits and opt-in capabilities. Use for untrusted code; supports a Python subset.
allowed-tools	monty_executor
metadata	{"author":"opencompany","version":"1.0","category":"code"}

Sandboxed Python (Monty) Tool

Execute Python in a deny-by-default sandbox powered by Pydantic Monty — a minimal Python interpreter written in Rust. Unlike the python_code tool (CPython exec with a restricted namespace), Monty enforces wall-clock and memory limits and grants zero host access unless you explicitly request it.

Prefer this tool when running code you don't fully trust, or when you need guaranteed time/memory bounds. Use python_code instead when you need libraries Monty doesn't support (e.g. random, collections) or language features it lacks (classes, generators).

How It Works

Connect the Monty Executor node to an agent's input-tools handle. The LLM calls the sandboxed_python tool with code (and optionally capabilities, timeout, max_memory_mb).

Schema Fields

Field	Type	Required	Default	Description
code	string	Yes	—	Python code to run in the sandbox
timeout	int	No	30	Max wall-clock seconds (1–600), enforced
max_memory_mb	int	No	256	Max memory in MB (16–2048), enforced
capabilities	string[]	No	[]	Host grants to enable (see below); empty = no host access

Inputs & Output

• input_data — a dict of upstream node outputs is available as the variable input_data. Read with input_data.get('someNode', {}).
• Return a result by making it the last expression in your code (e.g. a final line that is just output or a dict literal).
• print(...) output is captured and returned as console_output.

Supported Python Subset

Works	Does NOT work
def, closures, lambda	class definitions
if / for / while	yield / generators
try / except	with statements (without a workspace mount)
list / dict / set comprehensions	match / case
f-strings	import random, import collections, import os
async def / await	arbitrary third-party packages
import math, import json, import re

If you hit an unsupported feature, rewrite without it or switch to the python_code tool.

Capabilities (opt-in host access)

By default the sandbox has no filesystem, network, or environment access. Request only what the task needs via capabilities:

Capability	Grants	In-sandbox usage
http_get	An http_get(url) function (public http/https only; private/loopback hosts blocked)	body = http_get("https://example.com")
workspace_read	Read-only mount of the workflow workspace at /workspace	open("/workspace/data.txt").read()
workspace_write	Read-write mount at /workspace	open("/workspace/out.txt", "w").write(text)

Requesting more than necessary is discouraged — each capability is a deliberate hole in the sandbox.

Examples

Basic calculation (no capabilities):

{
  "code": "total = sum(range(1, 11))\nprint(f'sum 1..10 = {total}')\ntotal"
}

Process upstream data:

{
  "code": "nums = input_data.get('start', {}).get('numbers', [1,2,3])\navg = sum(nums) / len(nums)\nprint(f'avg = {avg}')\n{'avg': avg, 'count': len(nums)}"
}

Use the curated stdlib:

{
  "code": "import math, json\nr = 5\narea = math.pi * r ** 2\njson.dumps({'radius': r, 'area': round(area, 2)})"
}

Fetch a URL (requires http_get):

{
  "code": "body = http_get('https://example.com')\nlen(body)",
  "capabilities": ["http_get"]
}

Read a workspace file (requires workspace_read):

{
  "code": "data = open('/workspace/input.txt').read()\nlen(data.splitlines())",
  "capabilities": ["workspace_read"]
}

Enforced timeout (this will be terminated, not hang):

{
  "code": "while True:\n    pass",
  "timeout": 1
}

Guidelines

1. Return via the last expression — don't rely on a magic output variable; the final expression's value is returned.
2. Use print() for debugging — captured as console_output.
3. Request minimal capabilities — start with none; add only what the task needs.
4. Keep limits sane — timeout and max_memory_mb are hard caps; raise them only when justified.
5. Unsupported feature? Rewrite without it, or fall back to the python_code tool.