Monty is a minimal Python interpreter written in Rust that runs AI-generated code in a restricted environment without direct filesystem, environment-variable, or network access. It is for AI agents that need to execute Python and type-checking tasks while allowing developers to control which host functions and objects the code can use, and the catalogue entries integrate it into agent workflows.
Borrowing it
Nothing to install: this file belongs to pydantic/monty. 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/pydantic/monty/main/.agents/skills/python-playground/SKILL.mdgit clone --depth 1 https://github.com/pydantic/montyWrote 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/skills/pydantic/monty/python-playground)<a href="https://agentmods.dev/skills/pydantic/monty/python-playground"><img src="https://agentmods.dev/badge/skills/pydantic/monty/python-playground/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/skills/pydantic/monty/python-playground"><img src="https://agentmods.dev/badge/skills/pydantic/monty/python-playground.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00044 | $0.00378 |
| Opus 5.5 | $0.00018 | $0.00151 |
| Sonnet 5.5 | $0.00009 | $0.00076 |
| Haiku 4.5 | $0.00004 | $0.00038 |
Grade A, and why
python-playground 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 1mo 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.
What it actually says
Python Playground
Run Python code in an isolated playground directory for testing and experimentation.
Instructions
- First, ensure the playground directory exists. If the
playgrounddirectory doesn't already exist, runmkdir playground. - Use the Write tool to create the Python file at
playground/test.py - Run with:
uv run playground/test.pyto test cpython behavior orcargo run -- playground/test.pyto test monty behavior
IMPORTANT: Use separate tool calls for each step - do NOT chain commands with &&. This allows the pre-approved commands to work without prompting.
Example workflow
Step 1 - Create directory if it doesn't already exist (Bash, already allowed):
mkdir playground
Step 2 - Write code (use Write tool, not cat):
Write to playground/test.py:
def foo():
raise ValueError('test')
foo()
Step 3 - Run script (Bash, already allowed):
uv run playground/test.py
Guidelines
- The
playground/directory is gitignored - Use a different file name for each test you want to run, give the files recognizable names like
test_value_error.py - Use
uv run ...to run scripts (uses project Python) - Or,
cargo run -- ...to run scripts using Monty - Use Write tool for creating files (avoids permission prompts)
- Run mkdir and uv as separate commands (not chained)
- do NOT delete files from playground after you've finished testing
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.
- 1mo ago Changed d9b93016d195
- 2mo ago First seen · 51 lines · 44 tokens per session scan A 543d16ac8f2a
python-playground is a skill published in the GitHub repository pydantic/monty (8,801 stars, last pushed today), licensed MIT. It adds 44 tokens to every session and 378 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-30.
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Guidelines for writing and running tests in the Agent Framework Python codebase. Use this when creating, modifying, or running tests.
adk-verify-snippets
Checks that every Python code block in a Markdown file actually compiles and runs, by extracting each block to a temporary file, executing it in an isolated subprocess, and writing a pass/fail report with per-snippet coverage. Use when the user asks to verify, test, or validate the code samples in a README, a guide…
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Sets up a local ADK Python development environment in a git clone of the open-source adk-python repository: a uv virtual environment, all dependency extras, pre-commit hooks, and a first unit-test run. Runs only when explicitly requested, never on its own. Use when asked to set up, bootstrap, or repair a development…
test-generator
Generate pytest test cases for Python functions and classes.
kb-python-development
Reference data, not a reviewer. Python and wxPython development: packaging (PyInstaller/Nuitka), testing, desktop accessibility APIs, cross-platform paths, and framework patterns.