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 5.5 | $0.00057 | $0.01390 |
| Sonnet 5.5 | $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 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.
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.
- 2mo 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 (2,323 stars, last pushed today), licensed MIT. It adds 142 tokens to every session and 3,474 once invoked, about $0.0006 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.
Other skills, from other repositories
autoreview
Structured code review through the shared OpenClaw agent-skills installation.
omh-code-review
This is an OMH code-review workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).
revdiff-plan
Review the last Codex assistant message (plan, analysis, or proposal) with inline annotations in a TUI overlay. Extracts the most recent response from Codex rollout files and opens it in revdiff for review and annotation. Activates on "revdiff-plan", "review plan with revdiff", "annotate plan", "review last response"…
explore-unknowns
Guide the user through a quadrant walk that maps the unknowns of a task — open by listing the known knowns, then work through known unknowns, unknown knowns, and unknown unknowns one stage at a time, ending with a complete four-quadrant map in the user's hands. Use when a request is ambiguous or underspecified, the…
re0-merge
Review and land an external contribution the way this suite does: gate it against the thesis, land it with the author's credit intact, complete a new skill rather than merging it raw, then approve, credit, and explain before closing. Use when reviewing a pull request, as any collaborator or maintainer, not only the…
asplos-writing-style
Use when drafting or revising ASPLOS prose — engineering the first two pages that rapid review actually reads, stating the cross-layer insight as one quotable sentence, structuring per-layer mechanism sections, writing bounded claims, and fitting everything into 11 self-contained pages that include figures and…