World of ClaudeCraft is a browser-based classic-style multiplayer online game with a persistent shared world that can also run locally or be controlled through a Python reinforcement-learning interface. Players can quest and raid in the online world, while developers can host it themselves and train AI agents to play. The catalogue skills, agents, instructions, hooks, and setting support workflows for interacting with and developing the game.
Borrowing it
Nothing to install: this file belongs to levy-street/world-of-claudecraft. 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/levy-street/world-of-claudecraft/main/.claude/skills/review-pr/SKILL.mdgit clone --depth 1 https://github.com/levy-street/world-of-claudecraftWrote 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/levy-street/world-of-claudecraft/review-pr)<a href="https://agentmods.dev/skills/levy-street/world-of-claudecraft/review-pr"><img src="https://agentmods.dev/badge/skills/levy-street/world-of-claudecraft/review-pr/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/levy-street/world-of-claudecraft/review-pr"><img src="https://agentmods.dev/badge/skills/levy-street/world-of-claudecraft/review-pr.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 155 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00142 | $0.03474 |
| Opus 5 | $0.00071 | $0.01737 |
| Sonnet 5 | $0.00028 | $0.00695 |
| Haiku 4.5 | $0.00014 | $0.00347 |
Grade A, and why
review-pr 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 13d 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.
How it starts
The opening of the file, as written. The whole thing — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review a PR (World of ClaudeCraft house style)
This repo takes many small AI-authored contributions, so review is the gate that keeps the invariants intact. A good review here is not a vibe check of the diff: it verifies the load-bearing claims against the actual code, names issues with file:line evidence and a severity, and reads like a calm human wrote it. Work through the steps below.
/code-review reviews the local working tree; THIS skill reviews a GitHub PR end to
end and posts the result. They are different jobs.
What a good review looks like (the voice)
- Short, calm, plain GitHub style. Write like a person, not an AI: no preamble, no summary-of-a-summary, no "Great work!" throat-clearing, no bulleted restatement of the diff, no hedging boilerplate. If a sentence sounds like a model wrote it, cut it.
- No em dashes or en dashes, and no emojis. Use commas, colons, parentheses, or "to" for ranges. (You are reviewing a repo that bans them; do not introduce them yourself.)
- Lead with what is genuinely good when it is good, then the issues. Do not flatter.
- Every finding carries a severity and evidence:
blocking/should-fix/nit, with afile:linepointer and a one-line why. - i18n: review English only. The full policy (what to check, what never to raise) lives in the i18n domain block under Step 2; do not improvise beyond it.
- Post as a plain comment review (not approve / request-changes) unless told otherwise.
- Match the depth to the change: a one-file fix gets a tight note; a sim/wire/auth change earns the full invariant pass.
Step 1: Gather and scope
gh pr view <n> --json title,body,author,headRefName,baseRefName,state,additions,deletions,changedFiles,mergeable,mergeStateStatus,commits
gh pr diff <n> --name-only # which files, which domains
gh pr diff <n> # the diff (skip the generated i18n blocks)
git fetch origin pull/<n>/head:pr-<n>-review # local ref at the PR head
git fetch origin <baseRefName>
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.
- 13d ago First seen · 214 lines · 142 tokens per session scan A c6cf3b2767b0
review-pr is a skill published in the GitHub repository levy-street/world-of-claudecraft (2,251 stars, last pushed yesterday), licensed MIT. It adds 142 tokens to every session and 3,474 once invoked, about $0.0007 per session on Opus 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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Implementation + audit loop using parallel agent teams with structured simplify, harden, and document passes. Spawns implementation agents to do the work, then audit agents to find complexity, security gaps, and spec deviations, then loops until code compiles cleanly, all tests pass, and auditors find zero issues or…