Official agent skill

Review Usability

by pydantic in pydantic/monty

Check whether the common Python code an LLM would plausibly write still works on this branch, testing real cases in ./playground against CPython.

OfficialMITAuto-check passedFrontend & Design

Install Review Usability

skills CLI
$ npx skills add pydantic/monty --skill review-usability -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install pydantic/monty review-usability --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/pydantic/monty.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/review-usability .claude/skills/review-usability && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
review-usability
GitHub stars
8.6k
Token cost
~410 tokens
SKILL.md length
196 words
Files
1
Skills in repo
9
Repo updated
First seen
Licence
MIT

At a glance

Check whether the common Python code an LLM would plausibly write still works on this branch, testing real cases in ./playground against CPython.

  • Works in 4 steps: For each feature the branch touches,… → Write real test files in playground/… → Run each under both and diff → …
  • Find behaviour that diverges from CPython
  • Calls git
  • Trips up ordinary idiomatic code

What it does

Review Usability is an agent skill from pydantic/monty, published by the product's own GitHub organization. Check whether the common Python code an LLM would plausibly write still works on this branch, testing real cases in ./playground against CPython. Use to find behaviour that diverges from CPython or trips up ordinary idiomatic code.

Its SKILL.md is about 410 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Frontend & Design, covering UX design. It works with Python. The repository describes itself as: A minimal, secure Python interpreter written in Rust for use by AI. The licence is MIT.

When your agent uses it

  • Find behaviour that diverges from CPython
  • Trips up ordinary idiomatic code

Example prompts

  • “/review-usability”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. For each feature the branch touches, list the idioms a model reaches for first — the
  2. Write real test files in playground/ (see python-playground), named recognisably.
  3. Run each under both and diff
  4. Prioritise silent divergence — same code, different result — over a clean

What it can do on your machine

Read from SKILL.md and the folder at commit 5915273. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Review Usability loads about 410 tokens when it runs. Until then it costs about 62 tokens; SKILL.md has 196 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~62
When it runs · the whole SKILL.md, loaded when a task matches
~410

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from pydantic/monty at commit 5915273, republished under its MIT licence (© pydantic). 196 words, ~410 tokens.

Download SKILL.mdSave it as .claude/skills/review-usability/SKILL.md (or your agent's skills folder).
name
review-usability
description
Check whether the common Python code an LLM would plausibly write still works on this branch, testing real cases in ./playground against CPython. Use to find behaviour that diverges from CPython or trips up ordinary idiomatic code.

Usability review

Monty exists so LLMs can write Python that calls tools. Real usage is therefore the most common patterns, not exotic corners — and a divergence in a common pattern is the worst kind of bug, because the model has no way to know it must write something else.

Think hard on this one.

bash
git diff origin/main...HEAD
  1. For each feature the branch touches, list the idioms a model reaches for first — the obvious method, argument form, combination with another builtin. Include ones the branch does not handle; that's where the gaps are.

  2. Write real test files in playground/ (see python-playground), named recognisably.

  3. Run each under both and diff:

    bash
    uv run playground/test_thing.py        # CPython
    cargo run -- playground/test_thing.py  # Monty
  4. Prioritise silent divergence — same code, different result — over a clean AttributeError. A missing feature that raises is recoverable; a wrong answer isn't.

An undocumented divergence is also a ./limitations/ finding.

Report

Per divergence: the code, CPython's output, Monty's output, how likely a model is to write it. Then unsupported-but-common idioms with the error the user sees, and briefly what worked — it bounds the review. Leave the playground files in place.

Report only, unless the user asks for fixes.

© pydantic, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .agents/skills/review-usability of pydantic/monty.

Open the folder on GitHubat commit 5915273

Compare with similar skills

Review Usability next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Review Usability compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Review Usability this skillpydantic/monty8.6k—~410Automated safety check: PassMIT
Impeccablebestofjs/bestofjs3.1k27 repos~2.6kAutomated safety check: PassMIT
DocsPrefectHQ/fastmcp28k—~1kAutomated safety check: PassApache-2.0
Interface Design for Dashboards and Appsholaboss-ai/holaOS11k3 repos~6kAutomated safety check: PassMIT
Oil UIoil-oil/oil-ui1.1k—~1.8kAutomated safety check: PassMIT
Animategrowupanand/ConvoForm1026 repos~1.9kAutomated safety check: PassApache-2.0

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Works with

Questions about Review Usability

What does Review Usability do?

Check whether the common Python code an LLM would plausibly write still works on this branch, testing real cases in ./playground against CPython. Review Usability is an agent skill from pydantic/monty, published by the product's own GitHub organization./playground against CPython.

When should I use Review Usability?

Review Usability fits situations like: find behaviour that diverges from CPython; trips up ordinary idiomatic code.

How do I install Review Usability in Claude Code?

Run `npx skills add pydantic/monty --skill review-usability -a claude-code`. Or copy the skill folder (.agents/skills/review-usability in pydantic/monty) into .claude/skills/review-usability in your project. Claude Code loads it when a task matches its description.

How do I install Review Usability in Codex?

Run `npx skills add pydantic/monty --skill review-usability -a codex`. Or copy the skill folder (.agents/skills/review-usability in pydantic/monty) into .agents/skills/review-usability in your project. Codex loads it when a task matches its description.

Can I use Review Usability in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add pydantic/monty --skill review-usability -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/review-usability, .gemini/skills/review-usability, .github/skills/review-usability and .opencode/skills/review-usability in your project.

What does Review Usability need to run?

Going by SKILL.md and its folder, Review Usability needs the command-line tools its instructions call (git). Our summary lists: Python 3.

Does Review Usability access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Review Usability safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Review Usability use?

Review Usability is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Review Usability use?

About 410 tokens (SKILL.md is roughly 1.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Review Usability?

Skills that share tags, products or a category with Review Usability: Impeccable (bestofjs/bestofjs, 3.1k stars), Docs (PrefectHQ/fastmcp, 28k stars), Interface Design for Dashboards and Apps (holaboss-ai/holaOS, 11k stars) and Oil UI (oil-oil/oil-ui, 1.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Review Usability?

pydantic (a GitHub organization, an official publisher) maintains it in pydantic/monty, which has 8,607 GitHub stars. The repository holds 9 skills in this directory. The repository was last updated on October 9, 2026.

Source: pydantic/monty on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.