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/fastmod/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/fastmod)<a href="https://agentmods.dev/skills/pydantic/monty/fastmod"><img src="https://agentmods.dev/badge/skills/pydantic/monty/fastmod/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/fastmod"><img src="https://agentmods.dev/badge/skills/pydantic/monty/fastmod.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 | $0.00017 | $0.00173 |
| Opus 5.5 | $0.00007 | $0.00069 |
| Sonnet 5.5 | $0.00003 | $0.00035 |
| Haiku 4.5 | $0.00002 | $0.00017 |
Grade A, and why
fastmod 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 2mo 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
fastmod
Instructions
You can occasionally use fastmod or sed to make mass updates to the codebase and avoid wasting tokens changing each case one at a time.
Before making many repetitive changes to the codebase, consider using fastmod --accept-all.
THINK HARD about how best to use fastmod as it can dramatically improve your productivity.
Examples
Example of switching the py_type function to take heap: &Heap instead of a generic H: HeapAccess parameter:
fastmod --accept-all 'fn py_type<H: HeapAccess>\((.+?), heap: &H\b' 'fn py_type($1, heap: &Heap'
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.
- 2mo ago First seen · 23 lines · 17 tokens per session scan A 53a9f9b2e225
fastmod is a skill published in the GitHub repository pydantic/monty (8,801 stars, last pushed today), licensed MIT. It adds 17 tokens to every session and 173 once invoked, about $0.0001 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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